Participatory Design of an Activities-Based Collective Mentoring Program in After-School Care Settings: Connect, Promote, and Protect Program
Bibliographic record
Abstract
BACKGROUND: Out of school hours care (OSHC) services provide a unique opportunity to deliver early intervention programs to enhance primary school-aged children's social, emotional, physical, and cognitive well-being; however, such programs are currently lacking. OBJECTIVE: This study aims to address the lack of well-being programs for children accessing OSHC services in the research literature by using participatory design (PD) to collaboratively develop and test an OSHC well-being program-the connect, promote, and protect program (CP3). METHODS: The study employed methods of PD, user (acceptance) testing, and iterative knowledge translation to develop a novel well-being program framework-CP3-with key stakeholders (eg, children, OSHC staff, volunteers, families, clinicians, educators, and researchers). Thematic techniques were used to interpret and translate the qualitative information obtained during the research and design cycles. RESULTS: The co-design process generated the CP3 model, which comprises a group-based mentoring approach to facilitate enhanced activities in OSHC settings. Activities are underpinned by 4 key principles of program delivery: build well-being and resilience, broaden horizons, inspire and engage, and connect communities. CONCLUSIONS: To our knowledge, the CP3 program is the first co-designed well-being program developed specifically for OSHC services. This co-design process is key to ensuring local community needs-particularly those of young people accessing OSHC-are met and that these individuals are meaningfully and actively involved in all stages of the research and design process, from conception to implementation, evaluation, and continuous improvement.
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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 | 0.046 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".