Supervision de stage à distance : état de la recherche dans différents domaines de formation postsecondaire
Bibliographic record
Abstract
La supervision de stage à distance permet à des programmes d’institutions postsecondaires d’offrir un accompagnement en milieu de pratique qui remplace la supervision en présentiel. Malgré l’arrivée massive du numérique ouvrant la porte à une approche multidimensionnelle, nous en savons peu sur les pratiques des différents acteurs des stages dans ce contexte. Dans cet article, nous présentons les résultats d’une recension systématique de 43 articles sur le sujet. Par une analyse découlant des catégories de Wearne et al. (2015), nous comparons la supervision en présentiel à celle à distance, décrivons l’expérience des stagiaires et des accompagnateurs des milieux de pratique, en plus de dégager les principaux motifs, effets et enjeux issus de différents domaines de formation postsecondaire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".