Prioritising teachers’ health: The impact of ‘unstructured wellness time’ on educator wellness
Bibliographic record
Abstract
Objectives and Setting: Schools are becoming more and more complex work environments, in turn impacting teachers’ well-being. This study aimed to better understand how one teacher’s well-being could be impacted when offered consistent opportunities to attend to their own personal wellness during school hours through monthly ‘unstructured wellness time’. Design and Methods: Using autoethnography, the research is an account of first author’s personal experiences as an educator. Data collection and analysis was an iterative and holistic examination of critical incidents, reflective journal entries and photographs to allow for ‘meaning-making’ and to convey the first author’s personal experiences throughout the ‘unstructured wellness times’. A Comprehensive School Health framework was also used to reflect on and interpret data throughout this study. Results: Findings showed that being offered consistent time throughout the school year to attend to teacher wellness led to an increase in feeling that first author’s health was of more value within the workplace. Conclusion: Key conclusions drawn from the study contribute to the growing amount of literature on teacher well-being and identify that with the proper supports in place, the concept of ‘unstructured wellness time’ could be an effective tool to improving teachers’ health within school workplace settings.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".