Growing Pre‐Service Teachers' Well‐Being Capacity: Comparing the Perspectives of Teacher Education Programme Administrators and Teacher Candidates Across Canada
Bibliographic record
Abstract
ABSTRACT Recognising the importance of addressing teacher development in the early career stages, our study examined programmatic considerations within teacher education programmes in Canada to determine the extent to which teacher preparation included support for and promotion of teacher well‐being as part of their pre‐service teaching development. Using mixed method design, this article compares the findings from two pan‐Canadian questionnaires, from administrators of teacher education programmes and from teacher candidates in seven teacher education programmes across Canada, examining the programme components and ways they encouraged a focus on preparing for multiple dimensions of well‐being. The data analysis approach involved comparisons and contrasts between the two questionnaires (a set of the same or similar questions regarding dimensions of well‐being) and with the themes derived from the review of related research on fostering well‐being in educational programmes. The findings provide foundational knowledge and related practices that have the potential to guide the development of future teaching professionals.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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; 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".