Needs assessment for continuing education and health promotion training for Senegalese chief nurses/ Étude des besoins de formation continue en promotion de la santé pour les infirmières et infirmiers chefs de postes de santé (ICP) au Sénégal/ Estudio de las necesidades de formación permanente en promoción de la salud para las enfermeras y enfermeros jefe de los puestos de salud en Senegal
Bibliographic record
Abstract
This study, whose purpose is to contribute to improving chief nurses’ (ICP, French acronym) performance and practice in the realm of health promotion, was conducted in the medical region of Kaolak in Senegal. The objective is to identify the needs for ICP's continuing education and health promotion training and to delineate their priorities. This is a descriptive study characterised by a combined methodology which integrates a qualitative phase and a quantitative phase, in which six continuing education and training experts and 74 ICPs working in the region participated respectively. The method initially allowed the team to identify what are in theory the different types of health promotion skills that ICPs have, to validate this typology and it's contents through an expert panel and to adjust this to the practice of nursing at the community level in the Senegalese national context. Second, the range of training needs were measured by the ICPs and the classification of abilities was established in order of priority. This study allows for a comprehensive and detailed listing of needs for continuing education and training among Senegalese ICPs based on consensus on what their abilities are. The study also suggests that nurses’ initial education and training be adapted and continuing education and training be established.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".