The effect of model similarity on exercise self-efficacy among adults recovering from a stroke
Bibliographic record
Abstract
Background: People with physical disabilities, including those recovering from a stroke, often have functional impairments, which hinder their physical activity participation partially because they reduce their self-efficacy.Modeling has been suggested to be a promising solution to increase self-efficacy in those populations.However, research is required to test how different types of models can impact self-efficacy among adults recovering from a stroke.The purpose of this study was to examine changes in self-efficacy among adults recovering from a stroke in a post-stroke exercise program with peer and non-peer models.Methodology: We used an ABCA multiple baseline single-subject design with each letter representing a condition: (A) no model/Baseline 1 (three weeks); (B) peer model (six weeks); (C) non-peer model (six weeks); and (A) no model/Baseline 2 (three weeks).We recruited participants from Viomax, a Montreal fitness center for people with physical disabilities.A total of seven participants were recruited and four participants completed the study, still providing a level II evidence of single subject designs.Participants engaged in the weekly group exercise program 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.They completed two self-efficacy questionnaires after each weekly exercise session.Results: Higher self-efficacy levels were found for Participant 2 and 3 in the Peer Model and Non-Peer Model conditions when compared to Baseline 1.However, self-efficacy ratings appear to be the highest for the Non-Peer Model condition when considering the trend and level change analyses.Conclusion: These 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".