Développement d’un cours francophone en ligne sur les politiques publiques en santé : une collaboration internationale
Bibliographic record
Abstract
OBJECTIVE: To present the process and challenges of developing an online competency-based course on public health policy using a collaborative international approach. METHODS: Five public health experts, supported by an expert in educational technology, adopted a rigorous approach to the development of the course: a needs analysis, identification of objectives and competencies, development of a pedagogical scenario for each module and target, choice of teaching methods and learning activities, material to be identified or developed, and the responsibilities and tasks involved. RESULTS: The 2-credit (90-hour) graduate course consists of six modules including an integration module. The modules start with a variety of case studies: tobacco law (neutral packaging), supervised injection sites, housing, integrated services for the frail elderly, a prevention programme for mothers from disadvantaged backgrounds, and the obligatory use of bicycle helmets. In modules 1, 3, 4 and 5, students learn about different stages of the public policy development process: emergence, formulation and adoption, implementation and evaluation. Module 2 focuses on the importance of values and ideologies in public policy. The integration module allows the students to apply the knowledge learned and addresses the role of experts in public policy and ethical considerations. CONCLUSION: The course has been integrated into the graduate programmes of the participating universities and allows students to follow, at a distance, an innovative training programme.
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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 | 0.052 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".