Construire une vision commune de la formation des infirmières et sages-femmes en République Démocratique du Congo
Bibliographic record
Abstract
OBJECTIVE: To present the activities that facilitate the development of a public policy by public health and higher and university education ministry stakeholders - based on a common vision of nurses and midwives training in Democratic Republic of the Congo (DRC). METHODS: An operational research using different methods applied by experts called ?policy brokers? according to a framework covering the advocacy mechanisms (Advocacy Coalition Framework) designed to promote the development of a public policy. The population comprised 2 types of common interest groups (coalitions), derived from 3 systems (sociocultural-legal, educational, professional), involved in the choice of the ?secondary AND higher? or ?secondary OR higher? training profile for the concerned professionals. The methods comprised: workshops (discussion, training, restitution, validation, negotiation, scientific, reflection group meetings), training activities (programme development, training of nursing and midwives trainers-supervisors) and a variety of media coverage and marketing activities. RESULTS: The nurses and midwives profiles required in the DRC have been established. The levels required for their training have been validated and defined by a common vision of the two ministries concerned. A formal consultation framework was set up to launch the required reform for the review of these two professional's profiles. CONCLUSION: The public policy experts' activities based on the advocacy framework are complex, lengthy and time-consuming. In DRC, a Ministerial decree is currently being finalized to address the creation of a formal consultation framework concerning the training and utilisation of human health resources.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".