STRATEGIES USED BY PHYSICAL EDUCATION TEACHERS TO ENHANCE PROFESSIONAL WELL-BEING
Bibliographic record
Abstract
Disengagement from the teaching profession is a contemporary scourge among Canadian teachers.Quebec province is experimenting the same problematic situation.Diverse impacts are noticeable, associated to public costs, professional wearout, exhaustion and burnout.This research invites to an ideological and scientific shift in order to shed light on teachers' professional well-being.Positive psychology is used as theoretical framework to address the phenomenon, and better understand "What strategies do teachers use to create, maintain or restore a state of well-being?".To this end, the research objective is to draw a portrait of the different strategies used by teachers in order to enhance well-being.As a first stage of the project, the research protocol is aimed at teachers in physical education and health, since they are now recognized as leaders, models and promoters in healthy lifestyles in schools and communities.At the methodological level, we have undertaken five focus groups with 37 teachers, representing seven different regions of Quebec.The results will allow us to propose two avenues of strategies used by PE teachers in relation with Self and with Others.It is in this unprecedented perspective that we aspire to value the profession and the teachers, to favor the pursuit of professional careers and the motivation of teachers, and consequently to promote the educational success of pupils.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".