The effect of model similarity on exercise self-efficacy among adults recovering from a stroke: A mixed-methods study
Bibliographic record
Abstract
The purpose of this study was to examine changes in self-efficacy after adults of a post-stroke exercise program were demonstrated modelling from a peer and non-peer model. We used an ABCA multiple baseline single-subject design with each letter representing a condition: (A) no model/baseline 1 (3-weeks); (B) peer model (6-weeks); (C) non-peer model (6-weeks); and (A) no model/baseline 2 (3-weeks). We recruited participants from Viomax, a Montreal fitness center for persons with physical disabilities. Four participants engaged in the weekly group exercise program for 18 weeks and were presented with a peer model (a fellow person recovering for a stroke) and a non-peer model (a university student) during those respective conditions. Participants completed two self-efficacy questionnaires after each weekly session. Semi-structured interviews were conducted at weeks 9 and 18 of the program. Quantitative visual and trend analysis revealed higher self-efficacy levels for two participants in the peer model and non-peer model conditions when compared to baseline 1. However, self-efficacy ratings appeared to be the highest for the non-peer model condition. Thematic analysis revealed that participants preferred demonstrations from the models as opposed to explanations. Preference for the non-peer model could be because the participants generally had a better relationship with non-peer model. Results provide preliminary indication that modeling, in general, could help people recovering from a stroke increase their self-efficacy, with a slight advantage to non-peer models. Community organizations such as Viomax could implement models in their programs to help increase exercise self-efficacy of their members.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".