Outiller les futures et les nouvelles personnes enseignantes pour mieux cultiver leur bien-être, sans occulter l’importance des conditions d’entrée dans la profession
Bibliographic record
Abstract
Puisque plusieurs personnes enseignantes rapportent qu’elles vivent de la détresse psychologique, un nombre grandissant de personnes chercheuses recommandent de mieux les outiller dès la formation initiale, afin de faire face aux défis complexes de la profession. Conséquemment, nous avons invité des stagiaires (n = 44) à mettre en oeuvre des exercices pour assurer leur bien-être lors de leur dernier stage, puis à effectuer un bilan des retombées observées. Un suivi a aussi été réalisé via des entrevues avec quelques stagiaires (n = 5), au cours de leur insertion professionnelle. Le codage thématique des données, à partir des dimensions du modèle de bien-être PERMA+4, révèle que les exercices semblent contribuer à plusieurs de ces dimensions, ce qui offre des pistes prometteuses pour leur apporter un soutien à caractère psychologique.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".