Attentes et besoins des jeunes médecins pour un MOOC francophone sur la consultation avec les adolescents
Bibliographic record
Abstract
Adolescence is a pivotal period of human development, characterized by major biological, psychological, and social changes, and represents a key moment for health prevention. Yet, medical consultations with adolescents remain challenging for young doctors, mainly due to insufficient training. This thesis aims to identify the pedagogical needs of senior medical students in general practice and pediatrics in Montpellier and Quebec, in order to define the expected content and format of a french-speaking MOOC (Massive Open Online Course) on adolescent health consultations. A qualitative study was conducted through semi-structured interviews with ten young physicians. Thematic analysis revealed multiple obstacles: discomfort with sensitive topics, uncertainty about the appropriate relational posture, lack of a structured framework such as the HEADSSS method, professional isolation, and difficulty addressing both mental and somatic health. Despite these challenges, young doctors develop relational strategies empirically and express a strong need for concrete tools, theoretical benchmarks, and interactive, clinically relevant training. The proposed MOOC should offer a flexible and accessible format, including interactive clinical cases, guided video resources, testimonies from adolescents and professionals, and dedicated modules on sensitive issues. It would provide an innovative, collaborative, and structured response to the educational needs of young French-speaking doctors and a lever to improve adolescent health consultations and overall youth well-being.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".