Setting priorities for health interventions in developing countries: a review of empirical studies
Bibliographic record
Abstract
OBJECTIVE: To assess and summarize empirical studies on priority-setting in developing countries. METHODS: Literature review of empirical studies on priority-setting of health interventions in developing countries in Medline and EMBASE (Ovid) databases. RESULTS: Eighteen studies were identified and classified according to their characteristics and methodological approaches. All studies were published after 1999, mostly between 2006 and 2008. Study objectives and methodologies varied considerably. Most studies identified sets of relevant criteria for priority-setting (17/18) and involved different stakeholders as respondents (11/18). Studies used qualitative (8/15) or quantitative (3/15) techniques, or combinations of these (4/15) to elicit preferences from respondents. In a few studies, respondents deliberated on results (3/18). A minority of studies (7/18) resulted in a rank ordering of interventions. CONCLUSIONS: This review has revealed an increase in the number of empirical studies on priority-setting in developing countries in the past decade. Methods for explicit priority-setting are developing, being reported and are verifiable and replicable and can potentially lead to solutions for ad hoc policy-making in health care in many developing 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 | 0.032 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".