EMPOWERMENT AMONG FORMAL CAREGIVERS WORKING WITH PERSONS WITH DEMENTIA IN HOME CARE
Bibliographic record
Abstract
Abstract There is significant literature on workplace empowerment that focuses on individuals in positions of power rather than those who lack it. However, there is limited research on empowerment of home care workers, such as personal support workers (PSW) who work in dementia care. Empowerment is an active process based on a multifaceted model consisting of four components: meaning, self-determination, impact and competence. This study explored the roles of education and employer support in empowering PSWs to care for persons with dementia who live at home. Empowerment was investigated using semi-structured interviews with PSWs (N=15). A phenomenological approach was to understand the lived experiences of home-care based PSWs who work with persons with dementia. Components of empowerment were reflected through five emerging themes: “providing best care”, “autonomy”, “employer support”, “career long learning”, and “experiential learning”. The theme “providing best care possible” support the component of meaning, which included the motivation for training among PSWs and their value of aging in place. The theme “autonomy” supported the component of self-determination, which focused on PSW workload and feelings regarding their control working in home care versus long term care. The theme “employer support” supported the component impact, which included both PSW compensation and their perceived lack of emotional support. Finally, the themes “career-long learning” and “experiential learning”, were linked with impact and competence components, respectively. Overall, these findings support relationships between education and employer support in empowering PSWs who care for persons with dementia who live at home.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".