The Pan American Health Organization-adapted Hanlon method for prioritization of health programs
Bibliographic record
Abstract
OBJECTIVES: To document the underlying science of how the Pan American Health Organization (PAHO) adapted the Hanlon method, which prioritizes disease control programs, to its wider range of program areas and used it to implement the PAHO Strategic Plan 2014 - 2019. METHODS: In 2014, PAHO established a Strategic Plan Advisory Group (SPAG) with representatives from 12 Member States to work closely with the PAHO Technical Team to adapt the Hanlon method to disease and non-disease control programs. Three meetings were held in 2015 - 2016 during which SPAG reviewed existing priority-setting methods, assessed the original Hanlon method and subsequent revisions, and developed the adapted method. This project was initiated by Member States, facilitated by PAHO, and conducted jointly in transparent and horizontal technical cooperation. RESULTS: From the original Hanlon equation, the PAHO-adapted method maintains components A (size of problem), B (seriousness of problem), and C (effectiveness of intervention), drops component D (PEARL - Propriety, Economics, Acceptability, Resources, and Legality), and adds component E (inequity) and F (institutional positioning). The PEARL score was dropped because it serves a purpose for pre-screening process, but not in the priority-setting process for PAHO. CONCLUSIONS: The PAHO-adapted Hanlon method provides a refined approach for prioritizing public health programs that include disease and non-disease control areas. The method may be useful for the World Health Organization and country governments with similar needs.
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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 | 0.116 | 0.249 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".