Predicting sport and physical activity commitment and participation during early parenthood
Bibliographic record
Abstract
Despite considerable benefits of regular moderate-to-vigorous intensity physical activity (MVPA) and sport participation, adult participation rates remain low. Drawing from the sport commitment model (SCM), the purpose of this study was to examine predictors of sport commitment and MVPA, among parents of children (<13 years of age) across 3-months. Participants were randomly assigned to a team sport (n = 58, asked to select and participate in an adult team sport program), individual PA (n = 60, asked to select and participate in individual PA) or control (n = 66, asked to select a “date-night” activity) group and completed measures of the SCM (commitment, enjoyment, involvement opportunities, social constraints, investment, involvement alternatives) and self-reported MVPA at baseline, and post-randomization at six weeks and three months. Commitment and investment were significant predictors of MVPA over time, and enjoyment, involvement alternatives, and investment predicted commitment over time, controlling for group assignment. Individuals assigned to the team sport group reported greater commitment and investment at week 6 compared to the control group and higher investment partly mediated the relationship with MVPA. Interventions fostering enjoyment, and that can help support parents to make an investment to build commitment in sport and PA may be particularly effective when promoting MVPA in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".