Hockey FIT for Women (HFIT for Women): Evaluating reach and implementation of a gender-sensitized healthy lifestyle program
Bibliographic record
Abstract
Context: 57% of Canadian women have obesity or are overweight, indicating a need for novel mitigation strategies. Research suggests health promotion programs tailored to at-risk groups are more likely to be effective in improving health and sustaining implementation and outcomes long-term. Hockey FIT for Women (HFIT for Women) was adapted from Hockey Fans In Training (HFIT), a 12-week gender-sensitized healthy lifestyle program for men who were hockey fans with obesity/overweight. Process evaluation findings from both a HFIT pilot and large-scale trial indicated a need to adapt to women. Objective: Evaluate reach and implementation of the HFIT for Women program from participant and coach perspectives. Study Design & Analysis: Participant data was collected from an intake survey, virtual focus groups (n=2), and program exit survey (n=59). Coach data was collected through interviews (n=2). Transcripts and open-ended responses were analyzed thematically. Setting: Three local community fitness facilities and major junior hockey teams in Ontario, Canada. Population Studied: Participants included those who coached or completed the HFIT for Women program (i.e., identify as a woman, 18+ years of age, and passed safety screen). Intervention: HFIT for Women was 90 minutes (50% in-class education; 50% exercise), once a week, for 12 weeks. Outcome Measures: Focus groups, coach interviews, and exit surveys. Results: Majority of participants were white (98.2%), with a mean age of 41 years (±11.3 SD), and largely college educated (45.6%), employed (91.2%), and married (47.4%). Reasons for joining the program included a desire to connect with others sharing similar interests, and the experience of a partner or relative who completed HFIT. There was a desire for more novel and engaging information from both coaches and participants. Participants expressed their wish for more informal discussion time and competition to encourage each other, and coaches noted value in seeing the bonds created between participants. Suggestions focused on building a greater connection to hockey with competitive drills and playing floor hockey. Conclusion: Social connection was a key driver for participating in HFIT for Women. Future iterations should focus on leveraging the social connection through providing more group-based activities for the in-class portion of the program and increasing hockey-related competitive drills and games.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".