Perceptions of group exercise courses and instructors among Quebec adults
Bibliographic record
Abstract
BACKGROUND: Group exercise courses are popular among adults, but dropout rates are high. Studies of relationships between participants' perceptions and their participation might highlight factors to target to improve adherence and re-enrolment. METHODS: We used a mixed-methods approach to analyse perceptions of group exercise courses and instructors among 463 adults. Participants completed the Exercise Barriers and Benefits Scale, questionnaires on perceptions of the instructor and course, and non-participation. We assessed participation from weeks 2-4 and 5-10, and re-enrolment. We analysed relationships between perceptions and re-enrolment using linear regression and mediation analyses. We conducted group interviews with 11 participants. RESULTS: Predictors of re-enrolment included early participation (β=0.11, P=0.029) and perceptions of the group social climate (P=0.027). Perceptions of the group mediated the relationship between early participation and re-enrolment (95% CI 0.0036 to 0.0471): early participation predicted more positive perceptions (β=2.11, P=0.003), which predicted re-enrolment (β=0.01, P=0.006). Qualitative analyses highlighted instructors' roles in promoting social exchange and integrating participants into the group. CONCLUSIONS: The social climate of group exercise courses is a key factor predicting re-enrolment. Early participation predicts re-enrolment on its own, and also promotes positive perceptions of the group. Instructors can target these factors by sensitising participants to the importance of early participation, and promoting social exchange.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".