Primary Health care and Disasters—The Current State of the Literature: What We Know, Gaps and Next Steps
Bibliographic record
Abstract
INTRODUCTION: The 2009 Global Platform for Disaster Risk Reduction/Emergency Preparedness (DRR/EP) and the Hyogo Framework for Action 2005-2015 demonstrate increased international commitment to DRR/EP in addition to response and recovery. In addition, the World Health Report 2008 has re-focused the world's attention on the renewal of Primary Health Care (PHC) as a set of values/principles for all sectors. Evidence suggests that access to comprehensive PHC improves health outcomes and an integrated PHC approach may improve health in low income countries (LICs). Strong PHC health systems can provide stronger health emergency management, which reinforce each other for healthier communities. PROBLEM: The global re-emphasis of PHC recently necessitates the health sector and the broader disaster community to consider health emergency management from the perspective of PHC. How PHC is being described in the literature related to disasters and the quality of this literature is reviewed. Identifying which topics/lessons learned are being published helps to identify key lessons learned, gaps and future directions. METHODS: Fourteen major scientific and grey literature databases searched. Primary Health Care or Primary Care coupled with the term disaster was searched (title or abstract). The 2009 ISDR definition of disaster and the 1978 World Health Organization definition of Primary Health Care were used. 119 articles resulted. RESULTS: Literature characteristics; 16% research papers, only 29% target LICs, 8% of authors were from LICs, 7% clearly defined PHC, 50% used PHC to denote care provided by clinicians and 4% cited PHC values and principles. Most topics related to disaster response. Key topics; true need for PHC, mental health, chronic disease, models of PHC, importance of PHC soon after a natural disaster relative to acute care, methods of surge capacity, utilization patterns in recovery, access to vulnerable populations, rebuilding with the PHC approach and using current PHC infrastructure to build capacity for disasters. CONCLUSIONS: Primary Health Care is very important for effective health emergency management during response and recovery, but also for risk reduction, including preparedness. There is need to; increase the quality of this research, clarify terminology, encourage paper authorship from LICs, develop and validate PHC- specific disaster indicators and to encourage organizations involved in PHC disaster activities to publish data. Lessons learned from high-income countries need contextual analysis about applicability in low-income countries.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".