Is physical activity a part of who I am? A review and meta-analysis of identity, schema and physical activity
Bibliographic record
Abstract
Two parallel literatures on the physical activity (PA) identity and schema constructs have the potential to supplement traditional social cognitive approaches used for PA promotion. The purpose of this paper was to review schema/identity research and appraise its relationship with PA via meta-analysis followed by thematic analyses of its correlates, as well as its proposed mechanisms on PA. Eligible studies were from English, peer-reviewed published articles that examined identity and/or schema in the context of PA. Searches were completed in June 2015 in five databases. Sixty-two independent data-sets (32 available for meta-analysis), primarily of modest quality, were identified. Results of the random effects meta-analysis showed that the point-estimate between identity/schema and behaviour was r = .44 (CI = .39-.48), and invariant to selected study characteristics. Thematic review showed that identity/schema was associated with commitment, ability, affective judgments, identified/integrated regulation and social comparison and predicted intention, self-regulatory efficacy, and self-regulation strategy use. It had reliable evidence as a moderator of the intention-behavior relationship, was associated with increases in the speed of processing of relevant information and created negative affect under hypothetical identity-behavior discrepant situations. While this initial research is promising, more rigorous research designs, including interventions to increase identity/schema, are warranted.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".