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Record W4366088656 · doi:10.21153/thl2023art1769

Bayanihan E-Konsulta: A volunteer-driven response to the COVID-19 pandemic in the Philippines

2023· article· en· W4366088656 on OpenAlexaff
Janine Patricia G Robredo, Raymond John S. Naguit, Keisha Mangalili

Bibliographic record

VenueThe Humanitarian Leader · 2023
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsInstitute on Governance
FundersUniversity of the PhilippinesUniversity of Santo Tomas
KeywordsAccountabilityHealth careCredibilityPublic relationsPopulationBusinessAgency (philosophy)Political scienceEconomic growthMedicineEnvironmental healthSociologyEconomics

Abstract

fetched live from OpenAlex

COVID-19 stretched health systems worldwide, but its deepest impacts were disproportionately felt across certain population segments. In the Philippines, a low-middle income country with one of the longest pandemic-induced lockdowns, the most marginalised communities suffered the most, and had little agency to afford and access care. Socioeconomic barriers, compounded by the misallocation of limited resources and the militarisation and overall mismanagement of the response, widened inequities, and resulted in poorer health outcomes for these groups. In an attempt to redress this, the Office of the Vice President of the Philippines sought to fill gaps in health delivery and access by launching Bayanihan E-Konsulta (BEK), a free telemedicine platform for indigent Filipinos. Through a Facebook messenger service that ran on free data, patients were given the opportunity to consult with health professionals regarding their medical concerns at no cost. Relevant social services, such as prescription delivery, laboratory assistance, and food and financial aid, were also streamlined in the platform. Recognising limitations in funding, the program banked on the mobilisation of health professionals and volunteers, and relied on capacity building initiatives and the establishment of inter-agency collaborations. Institutional credibility, intersectoral collaboration, and effective management of team dynamics were identified as enabling factors for the program's effectiveness. Transparency attracted partnerships, and trust in leadership inspired solidarity, volunteerism, and continued service. Inclusivity in different project stages improved engagement and encouraged shared participation and accountability, allowing for resilience and sustained action. Overall, BEK stands as a successful example of a low-cost public/private/volunteer health response in a time of crisis. This paper discusses the critical challenges, considerations, and the iterations to the service implemented by the BEK team, providing insights for public health leaders and other low-to-middle income countries when tailoring responses to future public health emergencies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.173
GPT teacher head0.408
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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