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What Is a Case Study?
When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to learn all about case studies.
Deep Dive into a Topic
At face value, a case study is a deep dive into a topic. Case studies can be found in many fields, particularly across the social sciences and medicine. When you conduct a case study, you create a body of research based on an inquiry and related data from analysis of a group, individual or controlled research environment.
As a researcher, you can benefit from the analysis of case studies similar to inquiries you’re currently studying. Researchers often rely on case studies to answer questions that basic information and standard diagnostics cannot address.
Study a Pattern
One of the main objectives of a case study is to find a pattern that answers whatever the initial inquiry seeks to find. This might be a question about why college students are prone to certain eating habits or what mental health problems afflict house fire survivors. The researcher then collects data, either through observation or data research, and starts connecting the dots to find underlying behaviors or impacts of the sample group’s behavior.
During the study period, the researcher gathers evidence to back the observed patterns and future claims that’ll be derived from the data. Since case studies are usually presented in the professional environment, it’s not enough to simply have a theory and observational notes to back up a claim. Instead, the researcher must provide evidence to support the body of study and the resulting conclusions.
As the study progresses, the researcher develops a solid case to present to peers or a governing body. Case study presentation is important because it legitimizes the body of research and opens the findings to a broader analysis that may end up drawing a conclusion that’s more true to the data than what one or two researchers might establish. The presentation might be formal or casual, depending on the case study itself.
Once the body of research is established, it’s time to draw conclusions from the case study. As with all social sciences studies, conclusions from one researcher shouldn’t necessarily be taken as gospel, but they’re helpful for advancing the body of knowledge in a given field. For that purpose, they’re an invaluable way of gathering new material and presenting ideas that others in the field can learn from and expand upon.
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Optimizing health facility location for universal health care: A case study from the Philippines
Lorenzo jaime yu flores.
1 Department of Statistics and Data Science, Yale University, New Haven, Connecticut, United States of America
Ramon Rafael Tonato
2 Health Facility Development Bureau, Department of Health, Manila, Philippines
Gabrielle Ann dela Paz
Valerie gilbert ulep.
3 Philippine Institute for Development Studies, Quezon City, Philippines
Interested researchers can replicate the results of the study through the listed third party data and protocol in the Methods section. The code provided in the GitHub repository lists all the steps taken and will replicate the results. Our results were based on a brute force algorithm that calculates the optimization metric for all combinations of site sets, which means that the results should stay the same across runs. The datasets underlying this study are available from the following URLs GitHub repository: https://github.com/ljyflores/facility-location-philippines WorldPop Dataset (Population): https://www.worldpop.org/geodata/summary?id=6316 National Health Facility Registry (Rural Health Units): https://nhfr.doh.gov.ph/rfacilities2list.php PhilGIS (Philippine Map Shapefiles): http://philgis.org/category/administrative-boundaries?page=2 Philippine Statistics Authority Population Data (Antipolo City Statistics): https://www.psa.gov.ph/sites/default/files/attachments/hsd/pressrelease/R04A.xlsx .
Site selection of health facilities is critical in ensuring universal access to basic healthcare services. However, in many low and middle-income countries (LMICs) like the Philippines, site selection is traditionally based on political and pragmatic considerations. Moreover, literature that demonstrates the application of facility location models in the Philippine healthcare setting remains scarce, and their usage in actual facility planning is even more limited. In this study, we proposed a variation of cooperative covering maximal models to identify the optimal location of primary care facilities. We demonstrated the feasibility of implementing such a model by using open source data on an actual city in the Philippines. Our results generated multiple candidate locations of primary care facilities depending on the equity and efficiency parameters. This approach could be used as one of the critical considerations in evidence-based, multi-criterion health facility location decisions of governments, and can also be adapted in other industries, given the model’s use of readily available open source datasets.
Health facility location is a critical factor in strategic planning of healthcare programs [ 1 , 2 ]. A well-placed health facility increases uptake of essential healthcare services and improves health outcomes especially among vulnerable populations [ 3 , 4 ]. In many low and middle-income countries (LMICs), the decision to build health facilities is traditionally based on political and pragmatic considerations [ 5 ]. Consequently, the location of most health facilities is typically far from optimal [ 6 ]. In recent years, governments are now increasingly interested in studying where to build health facilities to facilitate the achievement of health system goals.
In the Philippines, access to basic healthcare services remains a major challenge. This is largely attributed to scarcity and maldistribution of health facilities in many parts of the country. About 50% of the population do not have access to primary care facilities (PCFs) within 30 minutes [ S1 File ]. To address this, the Philippine government passed a landmark legislation called the Universal Healthcare (UHC) Act in 2019, which outlined strategies for multiple demand and supply-side challenges that continued to impede universal access to essential healthcare services. One of the critical provisions of the law is to increase capital infrastructure investments in the medium to long-term. Relevant to the reform includes identifying optimal locations for new healthcare facilities, specifically primary care facilities (PCF) or rural health units (RHUs) , which are government-owned health facilities that provide basic and comprehensive healthcare services to individuals, families, and local communities. In this study, we focused mainly on primary care facilities to align with the goals of the UHC law , which involve augmenting the country’s primary healthcare system by the year 2025. Ultimately, the goal is to select and identify locations that serve the most people while still accounting for distance, hazards, and existence of other healthcare facilities.
In computer science, this task is known as the facility location problem (FLP), which has been adopted for many applications in healthcare, education, retail, etc. [ 1 , 7 – 11 ]. Typically, models solve this problem by using algorithms that determine the best placement of a facility that optimizes for metrics such as least average travel time to a facility or most coverage within some radius, with examples shown in Table 1 . The choice of model is based on the metrics that policy makers wish to optimize for. Therefore, there is no gold standard amongst facility location models, but rather a set of optimal locations chosen based on the priorities and goals of decision makers.
Outside the Philippines, multiple studies have demonstrated the application of these models towards optimizing the location of healthcare facilities. The team of [ 16 ] demonstrated the use of the Capacitated Maximal Covering Problem in Kuala Langat, Malaysia, which maximized the number of people living within 3km and 5km of rural clinics while accounting for limitations in capacity of each facility. A similar study was performed by [ 17 ] for sexual health clinics in Hampshire, United Kingdom, where they showed that a greedy solution achieved optimal or near optimal solutions when compared to other complex solving algorithms. The team of [ 18 ] proposed a multiple-objective model for a case study in Hong Kong, which maximized the number of people living within 10km of a health facility and an accessibility metric, while minimizing building cost and inequity. The team of [ 19 ] further optimized for metrics such as service quality and environmental concerns in facility location on their case study on Mao County, Sichuan, China.
In such studies, the ability to develop models that accounted for the mentioned variables relied on the availability of data. Some studies employed assumptions in the modeling process, while others required city-specific data collected for the study. This may pose challenges in practical application in countries where this data is not yet readily available, like in the Philippines.
As of this paper’s writing, similar literature that demonstrated the feasibility of implementing such models in the Philippine healthcare context remains scarce. Previous work applied a hierarchical location model to determine optimal placements of barangay (i.e. village) level clinics in Davao City, Philippines [ 5 ]. However, the work operated under the assumptions that (1) there were no existing health facilities, (2) candidate facilities would be placed at the centroid of each barangay assuming population was concentrated there, (3) travel distance between points was modeled using Euclidean distance, and (4) demand was the same all throughout the region. While the lack of data at the time explains why such assumptions had to be made previously, the advent of remote sensing based population modeling and advances in geospatial software have made granular data readily accessible, thereby allowing researchers to address these assumptions. For example, travel times calculated using road networks and driving speeds are available through APIs such as Mapbox; population at a granularity of 100m x 100m is available through datasets such as the WorldPop dataset developed by the Center for International Earth Science Information Network [ 20 ]; and a list of Philippine health facilities can be accessed at the National Health Facility Registry by the Philippine department of health, and their coordinates can be obtained using the Google Geotagging API [ 21 ].
The mentioned open source datasets can be publicly audited, and are thus relatively secure. Moreover, such data has little to no overhead or long-term costs compared to proprietary software, which makes it more preferable and advantageous in LMIC settings. Since the Philippine health system is devolved and many data collection systems are fragmented, using open source data can make it easier for different local government units to access, evaluate, modify and employ this method at their perusal. However, literature that demonstrates the feasibility of combining and using such data towards the facility location problem in the Philippine healthcare system context remains scarce, and the practical application of facility location modeling in the context of health facility development remains limited.
To this end, we explored a solution to the facility location problem that incorporates these open source datasets into the cooperative maximal covering model, which maximizes the number of people covered by the facilities, given a fixed number of sites to be built [ 22 ]. In this model, multiple health facilities could be used to cover each site, and the number of people which a facility attracts depends on the attractiveness of a site. This attractiveness can be modeled by distance decay , that is, the relationship between the travel time to a site, and people’s willingness to visit it. We employed the results from a recent work which modeled distance decay using hospital patient visit records in Florida, USA [ 23 ].
In this paper, we made the following contributions. First, we proposed metrics for evaluating the location of a new primary care facility that incorporated results from recent healthcare literature. Second, we demonstrated the feasibility of using open source data to calculate and optimize such metrics on an actual city in the Philippines. Third, we compared the locations chosen by each method and identified its implications on issues of healthcare equity. Ultimately, we aimed to further the literature on facility location modeling in the Philippine healthcare system context by outlining an end-to-end framework for primary care facility site selection to assist in government policy making. Through the use of open source, granular datasets, we aim to develop a model that can address limitations in previous work, and one that can be replicated across multiple cities through the use of readily available open source data. Moreover, this model can be further modified to perform similar analyses for other health facilities.
We used the open source datasets listed in Table 2 to conduct the analysis, and obtained the coordinates of PCFs in the National Health Facility Registry of the Philippine Department of Health (DOH) using the Google GeoTagging API. The Roads API provided the coordinates of the closest road segment to a given coordinate, based on existing road data in Google Maps. The Optimization API generated the driving time between two coordinates, based on the road segment’s length and given speed limits. The source code and datasets can be found at https://github.com/ljyflores/facility-location-philippines .
We chose Antipolo City, Philippines (2015 population: ~780,000 [ 25 ]) as a case study, as it comprises both urban and rural areas that highlight nuances between the proposed methods. Antipolo City is described as hilly and mountainous, with the hilly area in the west, and the mountainous areas in the east. Valleys are located in the urban area towards the southwest, and also in the south and north. Currently, there are 5 RHUs in Antipolo ( Fig 1 ).
We placed a grid with cells of size 1km x 1km over the map of Antipolo, and designated the center of each cell as a candidate site. We chose this granularity because of limitations in computational resources. Then, we used the Google Roads API to identify sites near existing roads. Only sites for which road segments were found by the API were kept.
We proposed two optimization metrics for policy makers to consider when selecting a goal to optimize for, and two demand adjustment methods which allow policy makers to adjust the weight given to populations that already have access to existing health facilities. The first metric considers the number of people living within a 30-min drive of a facility, which is the goal stipulated in the Philippine Health Facility Development Plan (PHFDP) [ S1 File ]. The second metric accounts for the number of expected visitors as suggested by [ 23 ] ( Eq 2 ).
where P is the population at site i , B is the number of beds at facility j , t i , j is the travel time in minutes from site i to facility j , and μ, α, σ, θ, β are constants.
In their paper, [ 23 ] proposed that the attractiveness of a facility is a product of (1) its capacity and (2) people’s willingness to travel to it. We set the capacity of each RHU at 20,000 people as specified in the PHFDP [ S1 File ]. Eq 1 modeled willingness to travel as a score between 0 and 1, based on experimental results performed in Florida, USA [ 23 ].
In this study, we adjusted demand to account for areas which are already “covered” by existing RHUs. In Method A (Zeroed Demand), we located areas within a 30-minute drive of an RHU, then set demand in those areas to 0. In effect, this excluded populations within 30 minutes of existing RHUs from the calculation, giving full priority to people without RHU access. In Method B, we reduced demand around an existing RHU (within a 30-minute drive) based on its capacity ( S1 Appendix ). This gave priority both to people without RHU access and those in areas where the capacity of existing RHUs could not adequately meet the demand. We compared our findings with results generated by algorithms with no demand readjustment employed. By applying such methods, the algorithms are optimized for areas with existing demand, often located in remote or underserved areas, which would help policy makers address issues of healthcare equity.
We extended the problem to a multiple facility problem, and presented the results for a two-facility optimization. For Metric 1, the code was written to find the total number of people living within a 30 minute drive of either one of the two facilities. For Metric 2, which accounted for the number of visitors, the algorithm was designed to eliminate duplication of demand ( S2 Appendix ). Once a site was chosen, the demand attracted by that site was added to its coverage score, then subtracted from the population. This also forced the algorithm to optimize for the remaining uncovered populations.
Finally, we validate our results following the procedure performed by [ 18 ]. First, we assume that there are no health facilities present, run the facility location model, and compute the selected optimization metric. Then, we compute the optimization metric based on the locations of the current RHUs. The expectation is that the locations selected by the algorithm perform at least as well as the current RHU system in terms of the selected metrics. We note that optimization metrics are merely one part of a multi-faceted decision process, and the optimality of the selected locations depends on multiple factors identified by local governments.
The results illustrated the strengths of each method and the associated tradeoffs. We baselined the results with simulations using unadjusted demand ( Fig 2A and 2D ).
(a) Metric 1, No demand adjustment, Buliran Rd, Brgy. San Luis (Near Philippine Intl. College), (b) Metric 1, Method A, Sumulong Hwy, Brgy. Mambugan, (Near Mambugan Brgy. Hall), (c) Metric 1, Method B, Magsaysay Ave, Brgy. Dela Paz, (Near Robinsons Place Antipolo), (d) Metric 2, No demand adjustment, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates), (e) Metric 1, Method A, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates), (f) Metric 1, Method B, Sumulong Hwy, Brgy. Santa Cruz, (Near Town and Country Estates).
Results using Metric 1 selected sites located directly in or adjacent to high population centers. The variations using no demand adjustment and Method B ( Fig 2A and 2C ) chose sites in the southeast part of Antipolo City (Brgy. San Luis/Dela Paz), while the one using Method A ( Fig 2B ) chose a site in Antipolo’s western side (Brgy. Mambugan). Compared to Metric 1, Metric 2 chose sites further away from individual population centers, and closer to the geometric center of Antipolo’s urban area ( Fig 2D–2F ), despite these areas being less populated. These results aligned with our intuitive understanding of the algorithms. Metric 1 was concerned with the population within 30-minute travel times, and thus selected localized high population sites. Metric 2 maximized visitorship from the entire city, and thereby chose more central locations.
We expected simulations using Method A (Zeroed demand) to select sites that were farther from existing RHUs, and Method B (Excess demand) to choose locations where existing demand was greatest, regardless of whether these sites were close to existing RHUs or not. Interestingly, both Methods A and B put facilities close to existing RHUs. This indicates that in Antipolo City, (1) highly populated areas either currently have or are located close to RHUs, but (2) these RHUs are likely inadequate to meet the demand in those areas. This scenario provided a second interpretation of the results. Instead of building new RHUs at the locations which the algorithm selected, local governments may consider expanding current facilities at the chosen sites to cater to existing or unserviced demand in the identified areas.
In the two-facility scenario, we found that the behavior of the metrics and methods were similar to that of the one-facility scenario ( Fig 3 ). Given that we increased the number of facilities, we expected Metric 2 to place one of the facilities closer to the center of the rural areas to attract visitors in that area. Interestingly, the results still chose two urban locations– which we can attribute to the parameters used in Eq 2 .
(a) Metric 1, No demand adjustment, Sumulong Hwy, Brgy. Mambugan (Near Mambugan Brgy. Hall) & Buliran Rd, Brgy. San Luis (Near Philippine International College), (b) Metric 1, Method A, Sumulong Hwy, Brgy. Mambugan (Near Villa Cecilia Subdivision) & Buliran Rd, Brgy. San Luis, (Near Philippine International College), (c) Metric 1, Method B, Sumulong Hwy, Brgy. Mambugan (Near Mambugan Brgy. Hall) & Buliran Rd, Brgy. San Luis (Near Philippine International College), (d) Metric 2, No demand adjustment, Sumulong Hwy, Brgy. Santa Cruz (Near Town and Country Estates) & Magsaysay Ave, Brgy. Dela Paz (Near Robinsons Place Antipolo), (e) Metric 1, Method A, Sumulong Hwy, Brgy. Santa Cruz (Near Town and Country Estates) & Sun Valley W Drive, Brgy. Inarawan (Near Forest Hills Golf & Country Club), (f) Metric 1, Method B, Sumulong Hwy, Brgy. Santa Cruz (Near Town and Country Estates) & Magsaysay Ave, Brgy. Dela Paz (Near Robinsons Place Antipolo).
In particular, the parameter θ, which we interpret as the travel time at which people are only half as willing to travel to a healthcare facility, was estimated to be θ = 6.29 minutes [ 23 ]. This will likely differ in the Philippines, where people in rural areas are accustomed to travelling for hours, or even days, to reach the closest health facility [ 26 ]. This implied the need to tune the parameters of the model and replicate the experiments of [ 23 ] in the Philippines, in order to yield more appropriate values.
We simulated results by adjusting θ in Eq 1 using the parameters estimated by [ 23 ] as a baseline ( Fig 4A ) and found that increasing θ causes the selected RHU sites to move away from individual population centers, and towards central or rural areas. This reflects the likelihood that people are more willing to travel farther to reach these central areas, and that central areas allow RHUs to expand their coverage. However, a consequence of this adjustment was that selected sites were now further away from residents living at the eastern side of the city, increasing travel costs for patients coming from geographically isolated and disadvantaged areas (GIDA). This raises issues of equity in healthcare resource allocation.
(a) θ = 6.29 minutes, (b) θ = 45.0 minutes, (c) θ = 60.0 minutes, (d) θ = 120.0 minutes, All simulations run with no demand readjustment, B = 20, μ = 0.20, α = 0.66, σ = 0.40, β = 2.14, as estimated by [ 23 ].
We explored the possible effects of each method on equity and presented them in Table 3 .
Finally, we validated that the algorithms that compute both metrics 1 and 2 achieve the expected performance, and present the results in Table 4 .
We find that the algorithm achieves better performance across both metrics, which confirms that the algorithms work as expected. The locations selected for the five RHUs for metrics 1 and 2 are shown below (Figs (Figs5 5 and and6 6 ).
Determining where to build a health facility is critical in ensuring equitable and efficient access to healthcare services. In this paper, we proposed a framework for selecting an optimal location for RHU site selection, by leveraging open source data and empirical work from previous healthcare studies. The choice of metric posed a tradeoff between optimizing for one localized population center (Metric 1) versus multiple population centers (Metric 2), while the choice of demand readjustment depended on which one weighs more in decision-making: prioritizing populations without RHU access (Method A), or including populations in areas where RHUs were insufficient to meet demand (Method B). Results that placed RHUs close to existing facilities also opened the possibility of expanding current RHUs instead of building new ones. These results differed based on the number of facilities to be constructed or upgraded. Ultimately, policy makers must weigh the issues of equity when deciding which outcomes to optimize for.
While our study proposed a framework to objectively identify the ‘best’ location where to build a health facility from an economic optimization perspective, it is still necessary for policy makers to develop a multi-criterion methodology or tool in health facility location decisions especially in low and middle-income countries (LMICs). Health facility decision-making is complex and may require different criteria, including cultural and socio-economic realities. Therefore, optimization models may be used to identify desirable candidate sites, but the final decision still requires multi-objective decision making tools [ 5 , 27 ].
There were four main limitations of the study. First, the framework did not identify available land for RHU construction. This can be addressed by collecting ground level data, or by using satellite imagery to classify these areas [ 28 – 30 ]. Second, the parameters in the distance decay model were not tuned to the local setting. Moreover [ 23 ], modeled the number of visitors using patient discharges in hospitals, not primary care facilities. These support the need for future work to replicate such studies in the local setting using the desired metrics. Third, the focus of the paper on RHUs merits the incorporation of private clinics into the analysis as they provide similar services [ 31 ]. As of this paper’s writing, there was no readily available dataset for private clinics, which indicated the need for further data collection. Finally, the paper only studied healthcare resource allocation through constructing RHUs–alternatives like building roads and improving transportation networks also affect healthcare access, which merit further study.
For future studies, researchers could extend our framework by examining the optimal location of primary care facilities, hospitals and other ancillary health facilities (e.g. stand-alone laboratories, pharmacies) in a given location. A well-functioning health system should have a robust referral network system, and the optimal distance among different types of health facilities is a critical factor in the efficient and equitable delivery of healthcare services.
S1 appendix, s2 appendix, acknowledgments.
The authors would like to thank Dr. Concepcion Lat, the city health officer of Antipolo City, for helping the authors validate the results of the algorithm with her on-the-ground knowledge of the city.
The authors received no specific funding for this work.
Guest Satisfaction Plan for Mystical Cave: A Case in Antipolo, Rizal
IOER International Multidisciplinary Research Journal, Volume 1, Issue 2, June 2019
10 Pages Posted: 12 Jul 2019 Last revised: 20 Jul 2019
Christine Grace P. de la Cruz
De La Salle University-Dasmarinas
Alyssa Mikhaela L. Dilao
Ernesto c. mandigma jr..
Date Written: June 30, 2019
Caves are one of the most important attractions the tourism industry has in a natural category. There are a lot of different caves within the country, but this study focused on the guest satisfaction of the Mystical Cave in Antipolo, Rizal about its guests’ demographic profile, assessment on its level of guest satisfaction and significant differences with the use of the concept of tour quality dimension. The study is quantitative research with a descriptive research design that used convenience sampling. Survey questionnaires were used in the data gathering process and were verified using Cronbach Alpha. The variables examined were gender, age, type of tourist, educational attainment, religion using frequency, and percentage. The guest satisfaction rating based on the TOURQUAL dimension, namely access, environment, the human element, experience, safety, and technical quality was analyzed through mean. The significant difference in the demographic profile and TOURQUAL dimension were analyzed through ANOVA and T-test. The results of the study showed that the type of tourist has a significant difference between the demographic profile and some of the tour quality dimensions, namely access, environment, the human element, experience, and safety. The guest satisfaction program proposed was designed to help the management of the cave to improve their tour quality in terms of access, environment, the human element, experience, safety, and technical quality to boost the satisfaction of the guests.
Keywords: Guest Satisfaction, TOURQUAL Dimension, Quantitative Research, Cave, Philippines
Suggested Citation: Suggested Citation
Christine Grace P. De la Cruz (Contact Author)
De la salle university-dasmarinas ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, consumer social responsibility ejournal.
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CASE STUDY: Urban Design THE CITY OF MARIKINA CASE STUDY MARIKINA CITY
In 2009 Metro Manila was caught off guard when Ondoy (Typhoon Ketsana) visited the country - billions of pesos worth of infrastructures and property was damaged and millions of liver were affected. The Manggahan floodway was built to prevent flooding in Metro Manila and ot would have been effective had informal settlers not congested the major waterway. Since disaster are not composed only of the natural hazard, but the human factor as well, the mere presence of informal settlements along the Manggahan floodway is not the sole cause of the problem; rather, it is a symptom of a larger problem of different facets. The solution to the problem of ineffective disaster mitigation is not merely technical, but it is also institutional, and more importantly social, thus, understanding the social aspects of disaster is a crucial part of the solution.
Alfredo Mahar Lagmay
The floods brought about by tropical storm Ondoy and typhoon Pepeng were the latest amongst the disasters that have plagued the Philippines year after year. In the last five years, catastrophes have inflicted thousands of deaths and costly damage to property amounting to billions of pesos. These disasters should be viewed not as one-off extreme events but as a manifestation of unresolved problems of planning and development. Proper contingency and developmental planning strategies in support of protecting life and property against natural hazards, is therefore an imperative. Following much-improved understanding of natural processes that underlie hazardous events, it is only with public policy application of technology as well as scientific (geophysical) and engineering knowledge that disasters can be effectively mitigated. This paper presents the major disasters in the Philippines over the last 5 years and the lessons learned from them. It also presents the key advanced technologies that can be used for disaster planning and how they can help mitigate natural calamities.
Alvin A . Camba
The article argues that in postwar Greater Manila, specifically from 1945 to 1960, conditions such as an open economy due to the postwar reconstruction process and the role of the government as an enabler of private interest culminated in a suburbanization process to the areas of greater Manila led by the private sector via the construction of subdivisions. Elites, who owned many of the real estate companies, took the opportunity to expand, to accumulate more land and capital. As part of a historical trend during the American period, land, then, became a privatized and highly contested commodity that effectively cemented class power in Greater Manila. Such actions resulted in the lack of public planning to answer the needs of many inhabitants of Greater Manila that are very visible until today. First, the article briefly discusses the concept of suburbanization, particularly its nuanced application in Southeast Asian cities and its relationship with established works. Second, the article discusses two related processes: the need of the reconstruction process for foreign investments to come in and the emerging economic nationalism during the period. The third section demonstrates the role of the state in urban planning and real estate in three levels—planning, land expropriation, and financial instruments. The final section elaborates on the impact of state policies, particularly suburbanization of the elites from 1950 to 1960.
Ma Regina Banaticla Altamirano
Karen May Abu
Pedro Ondo Angono
Arlene B Inocencio
Jhoana Dela Cruz
Robert John Robas
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Lorenzo Cordova Jr.
Niña Joy Idian
Segundo Joaquin E Romero, Jr.
J.R. Robert Real
Ricardo M Ribo Jr.
Michael Sto. Niño.
Lailanie Grace Murillo
Don Miguel Magturo
Espasyo (Journal of Philippine Architecture and Allied Arts)
Jose "Ding" A . Fernandez
Andrea Abundo , Celine Reusora
Leandro Nicholas R Poco
IOP Conference Series: Earth and Environmental Science
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- NORFIL Foundation, Inc. Liaison Services Agency Social Worker Quezon City Easy Apply 6d Assist the CCAs in the physical, emotional and psychological preparation of children that are accepted for adoption placement; Bachelor’s degree in Social Work.…
- Grain foundation for PWD inc. Project Team Leader Quezon City ₱20K - ₱25K (Employer est.) Easy Apply 22d Preferably graduate of BS in Social Work or any related bachelor's degree. Exhibit good judgment in making decisions that are related to the project.…
- St. Dominic Medical Center Registered Social Worker Bacoor Easy Apply 29d Maintains records of individual client for auditing by the COA.6. Represents SDMC TB-DOTS Clinic in meetings, trainings, and other related activities.…
- Cottolengo Filipino Inc. Caseworker, a registered social worker Rodriguez ₱15K (Employer est.) Easy Apply 30d+ Registered social worker with updated social work licensed. Responsible to handle case management such as reviews, implements and maintains updated client’s……
- FEU INSTITUTE OF TECHNOLOGY COMMUNITY EXTENSION ASSOCIATE Manila Easy Apply 27d Knowledgeable and do understand social issues and how public sector bodies work. Provide support to the Student Affairs and Community Services Office through a……
- St. Joseph's College of Quezon City Guidance Counselor Quezon City ₱19K (Employer est.) Easy Apply 12d Makes referrals when necessary to outside resources for student and /or family support; and creates awareness of the outside counseling resources to include……
- St. Joseph's College of Quezon City Guidance Counselor (BEd) Quezon City ₱19K (Employer est.) Easy Apply 30d+ Makes referrals when necessary to outside resources for student and /or family support; and creates awareness of the outside counselling resources to include……
- BP Tagaytay Foundation Inc Registered Social Worker, BP Tagaytay Tagaytay Easy Apply 30d+ BS Social Work graduate with RSW license. Previous experience in a non-government organization is an advantage. Case intake, profiling and management.…
- Bridges of Hope Lipa FACILITY SOCIAL WORKER (URGENT) Lipa Easy Apply 5d Assesses the socio-psycho status of the patients. With experience as Social Worker . With PRC *Social Worker * License. Job Types: Full-time, Permanent.…
social worker in Antipolo Jobs
Job outlook for social worker.
Social workers are in increased demand due to an aging population and the continual need for addressing issues ranging from physical and mental illnesses to addiction, homelessness and poverty. Entry-level positions do not require a bachelor’s degree and most jobs offer on the job training, with some states requiring social workers to be licensed. Social workers with higher education and specialized industry experience can earn greater compensation and expect to work at the management level.
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When you’re performing research as part of your job or for a school assignment, you’ll probably come across case studies that help you to learn more about the topic at hand. But what is a case study and why are they helpful? Read on to lear...
Case studies are important because they help make something being discussed more realistic for both teachers and learners. Case studies help students to see that what they have learned is not purely theoretical but instead can serve to crea...
Examples of a case study could be anything from researching why a single subject has nightmares when they sleep in their new apartment, to why a group of people feel uncomfortable in heavily populated areas. A case study is an in-depth anal...
Securing a Social Case Study Report. ABOUT THE SERVICE. A case report made by a social worker for indigent clients who will secure medical and other.
universal health care: A case study from the ... Statistics Authority Population Data (Antipolo City ... Social Science & Medicine.
We chose Antipolo City, Philippines (2015 population: ~780,000 ) as a case study, as it comprises both urban and rural areas that
The study is quantitative research with a descriptive research design that used convenience sampling. Survey questionnaires were used in the
Barangay Certification/Indigency/any valid ID; Social Case Study Report from LGU (optional); Referral/Endorsement letter from legislator, if applicable.
2.5 Submit Certificate of. Eligibility, Social Case Study. Report and referral letters to. Head Social Worker for review and approval. 3. Finance Unit/Service.
The solution to the problem of ineffective disaster mitigation is not merely technical, but it is also institutional, and more importantly social, thus
This study aims to analyze and understand the relationship of citizen satisfaction ... Some examples of.
urban, the municipalities of Baras and Antipolo still have populations.
Report from the City Government through the City Social Welfare and Development. Office. A Social Case Study Report contains basic information on the
Make social case study report that would serve as referrals to other government agencies. Must be a graduate of Bachelor of Science in Social Work.