Temporal Relationships of Self-Efficacy and Social Support as Predictors of Adherence in a 6-Month Strength-Training Program for Older Women
Bibliographic record
Abstract
The present study investigated how self-efficacy and social support predicted adherence to a strength training program for elderly women over two time periods in the initial 6 mo. of the program. Participants were 30 elderly women volunteers aged 75 to 80 who completed measures of barrier self-efficacy and general social support at baseline and 3 mo. later. Social support from the program was also measured at 3 mo. Adherence to the program was measured by attendance. Hierarchical regression equations were utilized to identify the contributions of self-efficacy and social support for adherence at 0 to 3 mo. and 4 to 6 mo. For prediction of the first 3 mo. of adherence, both self-efficacy and social support contributed significant unique variance towards the total explained variance of 36%. For the 4- to 6-mo. period, self-efficacy explained significant (12%) variance in adherence even when controlling for the previous 3-mo. adherence. Inclusion of general social support and social support from the program, however, did not account for significant variance. Researchers must continue to examine self-efficacy and social support in exercise adherence within various time periods among older adults to develop effective intervention strategies.
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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 | 0.007 |
| 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 | 0.001 | 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".