Quand les tâches façonnent le bien-être au travail : l’expérience de personnes enseignantes issues de deux provinces canadiennes
Bibliographic record
Abstract
Développer le bien-être enseignant est complexe, car les leviers ciblés, issus de disciplines variées, sont parfois éloignés de la réalité professionnelle. Cette étude examine les retombées d’aspects de la tâche enseignante sur le bien-être au travail à l’aide de deux échantillons (Nouveau-Brunswick [ n = 584] et Québec [ n = 510]). Les analyses suggèrent que la satisfaction à l’égard de la vie au travail ainsi que le bien-être psychologique peuvent être partiellement expliqués par des aspects de la tâche enseignante. Plusieurs tendances convergentes émergent entre les deux provinces. Elles mettent en lumière les particularités de la relation personne enseignante-élève et l’importance du leadership pour favoriser le bien-être des personnes enseignantes. Cette étude vise à orienter les politiques éducatives en fractionnant ce qui est compris sous l’expression «charge de travail».
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".