Exploring self-efficacy as perceived by men and women unpaid caregivers of older adults: A secondary analysis of focus group data
Bibliographic record
Abstract
• Self-efficacy scores should be interpreted cautiously. • Men and women caregivers of older adults view self-efficacy differently. • Women valued support from others to build their confidence. • Men preferred more independent strategies and resources. • Alternative outcomes may better reflect both men’s and women’s preferences. Psychoeducational interventions to enhance self-efficacy in unpaid caregivers of older adults have inconsistent impacts. This paper addresses the underexplored role of gender as a moderating factor by comparing caregivers’ lived experiences with the Generalized Self-Efficacy (GSE) scale’s conceptualization of self-efficacy. We conducted a secondary analysis of transcripts from eight focus groups with 45 unpaid caregivers in Canada. We applied both deductive (based on GSE categories) and inductive approaches to code focus group data, sorting it by gender. Findings revealed that GSE scale scores should be interpreted cautiously, as men and women perceive self-efficacy differently. Women valued external support and faced unique gender-specific challenges, while men preferred the independent strategies emphasized by the scale. Our findings provide context for interpreting GSE scores for men and women caregivers. For women, a low self-efficacy score may not indicate a problem, and alternative outcomes may more accurately capture their experiences.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".