Testing the physical activity self-definition model in the context of the ENCOURAGE trial
Bibliographic record
Abstract
Physical activity (PA) self-views are related to PA (Strachan & Whaley, 2013); little is known about how PA self-views develop. The PA self-definition model (PASD) outlines correlates of PASD. In this model, PASD is associated with PA commitment and ability. PA commitment is influenced by perceptions of PA wanting which is influenced by PA enjoyment. Perceptions of PA trying impact perceived commitment and perceived ability. Cross-sectional support for this model exists among active samples. Presently, the model is assessed among a previously inactive sample to determine if changes in model variables over the course of a PA program lead to change in PASD. Participants were 64 insufficiently-active people between the ages of 30 and 65 who completed ENCOURAGE (a quasi-experimental PA demonstration project). Upon physician recommendation, participants participated in 5 kinesiologist visits over 16 weeks to increase PA. At both baseline and intervention-end (16 weeks), participants completed measures of PA enjoyment, perceived wanting, trying, commitment and ability related to PA and PASD. Path analysis revealed that the PASD model was not supported (chi square = 47.79; RMSEA = .183; p < .001; CFI = .784). The addition of paths from perceived wanting to PASD and from perceived ability to commitment led to a model with acceptable fit (chi square = 16.20; p < .013; RMSEA = .11; CFI = .95). The additional paths may be explained by the previously inactive status of the present sample. PASD model variables should be directly targeted in future interventions to maximize increases in PASD.
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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 | 0.081 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".