Stakeholder Acceptability of the Hockey Fans In Training Healthy Lifestyle Intervention
Bibliographic record
Abstract
Context: Canadian men have a higher likelihood of having excess weight or obesity than Canadian women, which puts them at a significant risk of developing preventable chronic diseases. To address this issue, we created the Hockey Fans In Training (Hockey FIT) healthy lifestyle program designed for middle-aged male hockey fans with overweight or obesity. Objective: In parallel to a cluster randomised trial of Hockey FIT, we conducted a process evaluation of the acceptability of the Hockey FIT program from the perspective of program participants, implementation partners, and program coaches. This included identifying areas to be improved for future delivery beyond this trial. Study Design and Analysis: Data was collected through virtual focus groups (n = 8) with Hockey FIT participants (n = 34) and interviews with local program coaches (n = 16) and implementation partners (n = 21). A process of deductive analysis by question and inductive analysis by response was conducted by multiple members of the research team until data saturation was reached. Setting or Dataset: Program sites included a major junior or professional hockey team paired with a local implementation partner (e.g., fitness facility). Population Studied: Hockey FIT participants (i.e., men, aged 35-65 with a BMI ≥ 27 kg/m2), coaches, and implementation partners. Intervention/Instrument: Hockey FIT was a 12-week, group-based healthy lifestyle program delivered to middle-aged male hockey fans who have excess weight or obesity. Outcome Measures: Acceptability of the Hockey FIT program, current strengths of the initiative, and recommendations for future modifications. Results: Eight overarching themes emerged regarding the acceptability of the program and areas to optimize for future delivery. The themes included participants’ motivations for joining the program, the positive group dynamic, overall positive experience, the positive experience of program coaches, and the effective program components, such as the nutrition information. Opportunities for optimization and adaptation included incorporating more exercise earlier, greater connection to hockey, and improving the usability of app technology. Conclusions: Overall, the Hockey FIT program was perceived as acceptable with minor adaptations needed to improve delivery. The findings can inform future scale-up of the Hockey FIT program.
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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 | 0.043 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".