Exploring differences between gender expressions in exercise self-efficacy and outcome expectations for exercise in individuals with stroke
Bibliographic record
Abstract
Gender expression may be associated with exercise self-efficacy and outcome expectations for exercise in the general population. Exercising for challenge and enjoyment are associated with the instrumental traits typically held by individuals with masculine gender expressions. Conversely, exercising for weight loss to receive validation from others are in line with the expressive traits most commonly held by individuals with feminine gender expressions. Moreover, possessing neither dominant nor expressive traits (undifferentiated gender expressions) have been linked to poorer psychological outcomes. Exercise is important after stroke, but gender differences in psychosocial factors for exercise in this population were unknown. The purpose of this study was to explore whether gender expression differences exist in exercise self-efficacy and outcome expectations for exercise post-stroke. Gender expression (masculine, feminine, androgynous, undifferentiated) was assessed using the Bem Sex-Role Inventory-12 (BSRI-12) in 67 individuals with stroke. Self-efficacy and outcomes expectations for exercise were assessed using the Self-Efficacy for Physical Activity Scale and Short Outcome Expectations for Exercise Scale, respectively. One-way analysis of covariance models were conducted, adjusting for biological sex, age, and time post-stroke. There were differences in exercise self-efficacy across the four gender expression groups (F(3,60) = 4.28, p<0.01), where individuals with masculine gender expressions had higher self-efficacy than those with undifferentiated gender expressions (adjusted mean: 3.56 [SE: 0.17] vs. 2.72 [SE:0.18], p<0.01). There were no differences in outcome expectations for exercise (F(3,57) = 1.08, p = 0.36) between gender expressions. In our pairwise comparisons, we found that individuals with masculine gender expressions had higher exercise self-efficacy than individuals possessing undifferentiated gender expressions. Strategies to enhance exercise self-efficacy after stroke are needed, particularly for individuals with undifferentiated gender expression. There were no associations between gender expression and outcome expectations for exercise after stroke. Clinicians may continue reinforcing the positive expectations towards exercise across all gender expressions.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".