Investigating the Factors That Influence Self-Efficacy Among Home Care Workers Providing Dementia Care
Bibliographic record
Abstract
Abstract The self-efficacy beliefs of home care personal support workers (PSWs) play a crucial role in their professional competence and subsequent provision of quality care. Understanding the factors that influence self-efficacy of PSWs is critical to ensuring their job satisfaction and retention, and ultimately improving the quality of care provided to home care clients. Currently, there is a lack of literature exploring the factors influencing self-efficacy among home care PSWs who care for clients with dementia. Accordingly, the purpose of this study was to investigate the sources of self-efficacy for home care PSWs. Conventional content analysis of interviews with 15 home care PSWs yielded six categories of sources influencing self-efficacy: insufficient client information provided by employers, lack of supportive communication by employers, restriction of PSWs’ discretion and autonomy by employers, insufficient practical dementia-specific training, sufficient work experience with clients with dementia, and feedback from family caregivers. These findings call for a multi-pronged approach to enhance the self-efficacy of PSWs. In particular, these findings offer specific areas of improvement for employers on how to best support their PSWs. They also highlight the significant role of dementia-specific education and training for PSWs regardless of their experience in the field. Finally, the findings emphasize the importance of family caregivers in the home care context. Taken together, the study’s findings offer insights on how to best support PSWs and ensure stability in the dementia care workforce.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".