Enseigner en étant centré sur l’apprenant. Le témoignage concret d’une enseignante clinicienne
Bibliographic record
Abstract
Context: French departments of general medicine wish to implement longitudinal assessment of a resident’s progress. In Toulouse, intermediate objectives have been developed to track the evolution of resident competencies, an essential step for the certification. Aim: To develop a tool for assessing intermediate objectives. Method: A narrative review of the literature was carried out to identify the methods used in France and abroad to evaluate the progress of residents. In a second step, a working group of experts from the Faculty of Medicine at Toulouse University was set up to: (1) develop a tool to evaluate intermediate objectives; (2) propose a test phase during the summer semester of 2019 on a panel of interns and masters of training (MSU) from the Toulouse region. Results: We did not find in the literature any tool allowing a specific assessment towards competency in primary care. The tool we developed allows to trace the acquisition of intermediate objectives in two stages: at the second and sixth month of residency. The test phase evaluated its feasibility: 87% of the MSUs found the objectives relevant and 89% found the time required to complete the tool acceptable. Discussion: This concise tool would allow a specific measurement of the quality of interventions by residents in problems encountered in general medicine. It would complement existing evaluation tools for an inference of competency essential for the certification of a general practitioner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.020 |
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".