“I love this stuff!”: a Canadian case study of mentor–coach well-being
Bibliographic record
Abstract
Purpose The purpose of this paper is to report on a qualitative case study that examined the potential benefits, challenges and implications of the mentor–coach (MC) role as a supportive structure for experienced teachers’ well-being and sense of flourishing in schools. Design/methodology/approach The qualitative case study used data collected from surveys, interviews, focus groups and documentation. Data were coded and abductively analyzed using the “framework approach” with and against Seligman’s well-being PERMA framework. In order to include an alternative stakeholder perspective, data from a focus group with the district’s teacher union executive are also included. Findings Using the constituting elements of Seligman’s well-being (PERMA) framework, experienced teachers reported positive emotion, engagement, positive relationships, meaning and accomplishment from their MC experience. However, the MC role is not a panacea for educator well-being. Rather, the quality and effectiveness of the mentoring and coaching relationship is a determining factor and, if left unattended, negative experiences could contribute to their stress and increased workload. Research limitations/implications The data used in this study were based on a limited number of survey respondents (25/42) and the self-selection of the interview (n=7) and focus group participants (n=6). The research findings may lack generalizability and be positively skewed. Originality/value This study contributes to the current lack of empirical research on the MC experience and considers some of the wider contextual factors that impact effective mentoring and coaching programs for educators.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.062 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| 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".