Streaking and self-care planning: the influence of integrating a well-being initiative in one teacher education program
Bibliographic record
Abstract
Concerns about teacher burnout, compassion fatigue, and retaining early career professionals have prompted teacher education programs to seek out ways to promote mental and emotional health during pre-service teacher training.This two-year, educational design study explored pre-service teachers' experiences with the implementation of two school-based mental health interventions: the intentional introduction of self-care planning during field experience practicums, and the promotion of 'streaking', a health promotion event, to encourage pre-service teachers to implement a daily individual mental health intervention.Data were collected at several different points over a two-year period through surveys and individual interviews.The data analysis indicated that this twotiered approach holds promise as a health promotion activity, however other intentional and educational actions were needed to ensure that pre-service teachers had the resources and tools required to support their mental and emotional health during their practicum courses.A further finding suggested that the term 'self-care' was widely misunderstood by study participants and work is needed to help pre-service teachers understand the importance of self-care during their practicum as a prevention strategy for well-being once they begin their teaching career.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".