La charge de travail des agents de santé dans un contexte de gratuité des soins au Burkina Faso et au Niger
Bibliographic record
Abstract
User fees exemption policy supported by NGOs in Burkina Faso and Niger resulted in a higher utilization of health services in primary health care facilities. We conducted a survey in 2 health districts in Burkina Faso and Niger in 2011. The study objective was to assess whether the higher utilization associated with the user fees exemption policy, may result in an overload for health staff at the front line in health facilities. The WHO's recommended WISN method was used to compute a ratio of actual/required staff using a comparative study with 4 control facilities and 4 intervention sites where the user fees exemption policy was provided by local NGOs in both countries. Overall, 8 primary health facilities both in Burkina Faso and Niger were involved. In Burkina Faso, the ratio was ≥1 in all facilities both control and intervention, i.e. a sufficient staff in facilities. In Niger, 3 out of the 4 intervention facilities in Keita district were found to have a ratio ≤1, i.e. understaffed. In the 4 control facilities, the staff was sufficient with a ratio ≥1. In Burkina Faso, the actual number of staff in facilities appeared enough to face the higher utilization of health services that may follow the user fees exemption policy supported by local NGOs unlike Niger where we found that the actual number of staff was insufficient to face a possible higher utilization resulting from the same policy in intervention facilities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".