Calling non-governmental organisations to strengthen primary health care: Lessons following Alma-Ata
Bibliographic record
Abstract
BACKGROUND: The Alma-Ata Declaration's commitment to primary health care (PHC) reaches its 40th anniversary in 2018. Over the last 40 years, the number of non-governmental organisations (NGOs) working in low-income countries (LICs) has rapidly multiplied, and over time, NGOs have both positively and negatively impacted equity, effectiveness, appropriateness and efficiency of PHC systems in LICs. AIM: The authors aim to demonstrate that at the 40th anniversary of the Alma-Ata Declaration's commitment to PHC, NGOs are particularly poised to strengthen PHC in LICs. METHODS: In this letter, the authors reflect on how NGOs have both positively and negatively impacted equity, effectiveness, appropriateness and efficiency of PHC systems based on their experience working with NGOs in LICs. RESULTS: NGOs are poised to strengthen PHC in LICs in four distinct ways: assisting with local human resources development, strengthening local information systems, enabling community-based health services and testing innovative service delivery projects. CONCLUSIONS: The authors call for NGOs to commit their expertise and resources to long-term strengthening of PHC in LICs and to critically examine the factors that prevent or assist them in this goal. As the principles of Alma-Ata are renewed, NGOs should be responsibly engaged in strengthening the declaration's goal of 'health for all'.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".