Complexity of choice: Teachers’ and students’ experiences implementing a choice-based Comprehensive School Health model
Bibliographic record
Abstract
Background: Comprehensive School Health models offer a promising strategy to elicit changes in student health behaviours. To maximise the effect of such models, the active involvement of teachers and students in the change process is recommended. Objective: The goal of this project was to gain insight into the experiences and motivations of teachers and students involved in a choice-based Comprehensive School Health model – Health Promoting Secondary Schools (HPSS). Setting: School communities in British Columbia, Canada. Design and methods: HPSS engaged teachers and students in the planning and implementation of a whole-school health model aimed at improving the physical activity and eating behaviours of high school students. The intervention components were specifically informed by self-determination theory. A total of 23 teachers and 34 school committee members participated in focus group interviews. The minutes of planning meetings were collected throughout the intervention process. Results: Analysis of the data revealed five themes associated with participants’ experiences and motivational processes: (a) lack of time for planning and preparation; (b) resources, workshops and collaboration; (c) teacher control impacts student engagement; (d) teacher job action inhibited implementation of HPSS action plans; and (e) choice-based design impacts participants’ experiences. Conclusion: Findings from this study can facilitate future school-based projects by providing insights into student and teacher perspectives on the planning and implementation of school-based health promotion programmes and implementing choice-based educational change initiatives.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".