Student-centred photovoice as a mechanism for home-school interaction: Teacher perceptions of efficacy
Bibliographic record
Abstract
Objective: The school and home environments play a significant role in shaping the health behaviours of children. Understanding students’ home environments is essential for teachers to recognise and meet their students’ needs, while collaborative partnerships between the school and home have been shown to result in academic success and improved behaviour management. This study explores the unique features of photovoice as a student-centred approach to understanding the links between the school and home environments, and its feasibility to be implemented independently by teachers in the classroom. Design: Descriptive qualitative method. Setting: A Project Promoting healthy Living for Everyone in Schools (APPLE Schools) is a school-based health promotion project being conducted in 70 school communities across northern Alberta, Northwest Territories, and Manitoba, Canada. Method: One-on-one interviews with teachers who were involved in an initial photovoice project ( n = 3) and researcher field notes from observations conducted over a period of 8 months were used. Data were analysed using latent content analysis. Results: Strengths, limitations and future directions of photovoice were identified. The strengths of using photovoice included genuine student participation, strengthened communication between the school and home, and the ability to address multiple learning domains. Limitations were cost, privacy and parental support. Teachers shared promising ideas about photovoice being used for health promotion advocacy. Conclusion: Photovoice can be used by teachers as tool to strengthen the relationship between the home and the school environments. Future use of photovoice in schools is encouraged.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".