Developing physical activity interventions for adults with spinal cord injury. Part 1: A comparison of social cognitions across actors, intenders, and nonintenders.
Bibliographic record
Abstract
OBJECTIVE: This article is the first in a three-part series focused on designing theory-based interventions to increase leisure time physical activity (LTPA) in persons with spinal cord injury (SCI). The purpose of this first study was to compare social cognitions for LTPA between people classified as LTPA actors, intenders, and nonintenders, as per Schwarzer's Health Action Process Approach (HAPA) model. METHOD: Participants were 238 men and women living with a SCI (M age = 44.14, SD = 12.74; 44.5% paraplegic) who were subsequently classified as LTPA actors (n = 105), intenders (n = 73), or nonintenders (n = 60). Participants completed a questionnaire that assessed the following HAPA constructs: LTPA outcome expectancies, self-efficacy, intentions, planning, and action control. RESULTS: A MANCOVA revealed significant between-groups differences for all variables (ps < .001). For all of the measures, actors scored significantly higher than intenders who, in turn, scored significantly higher than nonintenders. CONCLUSION/IMPLICATIONS: It is both theoretically and practically important to distinguish between LTPA nonintenders, intenders, and actors when developing LTPA-enhancing interventions for people with SCI. These distinctions inform the design and testing of the interventions reported in the two accompanying articles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".