Extending health systems resilience into communities: a qualitative study with community-based actors providing health services during the COVID-19 pandemic in the Philippines
Bibliographic record
Abstract
Abstract Background Amidst ongoing calls for increased health systems resilience, gaps remain in our understanding of how health systems can reach further into communities to ensure resilient service delivery. Indeed, public health emergencies caused by infectious hazards reveal both the value and vulnerability of the workforce delivering health services in communities. This study explores ways in which a non-governmental organization (NGO) in the Philippines protected their frontline workforce during the first year of the COVID-19 pandemic. Methods Guided by a qualitative descriptive approach, 34 in-depth interviews were conducted with community-based health actors employed by the NGO between June 2020 and February 2021. Data analysis was guided by an iterative deductive and inductive approach. Results We identified four key activities that enabled the NGO and their staff to provide health and social services in communities in a safe and consistent manner as part of the organization’s pandemic response. These include (1) ensuring adequate personal protective equipment (PPE) and hygiene supplies; (2) providing contextualized and role-specific infection prevention and control (IPC) training; (3) ensuring access to testing for all staff; and (4) providing support during quarantine or isolation. Conclusion Learning from the implementation of these activities offers a way forward toward health emergency preparedness and response that is crucially needed for NGOs to safely leverage their workforce during pandemics. Further, we describe how community-based health actors employed by NGOs can contribute to broader health systems resilience in the context of health emergency preparedness and response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".