Facteurs contextuels influençant la délégation de tâches aux Agents de Santé Communautaire Polyvalents en Haïti
Bibliographic record
Abstract
Introduction: In Haiti, the delegation of tasks to Multi-skilled Community Health Workers (locally called ASCP) is a strategy implemented by the Ministry of Public Health and Population to improve universal health coverage. Purpose of research: To contribute to the successful implementation of this strategy, this article reports on a case study of its implementation in the northern health department of Haiti. More specifically, this article provides information on the contextual factors that facilitate or hinder the implementation of task delegation to ASCP. Results: The results obtained show notable progress in the implementation of task delegation to ASCP in the northern health department of Haiti. These mainly concern the following activities: selection, training, and deployment of ASCPs, endowment of work materials, supervision, collection and analysis of data on the results obtained. In September 2019, 215 ASCPs were active in the department. This corresponds to 44.3% of the 485 ASCPs planned to cover the department’s needs. Several contextual factors hindering or facilitating the implementation of this intervention were also identified with 35 resource persons during semi-structured interviews. These relate to the planning and monitoring of the implementation of the intervention (cited by 12 out of 35 people), the institutional context (10/35), and political (17/35), structural (30/35) and environmental factors (7/35). Conclusions: This study highlights several contextual factors that need to be considered to ensure the successful implementation of the delegation of tasks to ASCP in Haiti and possibly in other contexts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".