Working Conditions Supporting Person-Centered Care of Persons Living with Dementia in Long-Term Care Homes
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has underscored the importance of person-centered dementia care and working conditions that support such care in long-term care (LTC) home settings. Personal support workers (PSWs), known also as certified nursing assistants, provide the most direct formal care for persons living with dementia. However, little is known about the working conditions that enable person-centered care. Accordingly, the purpose of this study was to examine the working conditions and the impact of those conditions on PSWs in LTC homes. PSWs (N=39) employed at one of five LTC homes in southwestern Ontario, Canada participated in a series of one-hour focus groups before, during, and after Be-EPIC, a person-centred communication training program for formal caregivers of persons living with dementia. Using an interpretive description investigative framework, textual data from focus group conversation transcripts were open-coded into categories. Overarching themes were interpreted inductively. Study credibility was enhanced through investigator triangulation. Three themes emerged related to working conditions of PSWs: dementia care is complex, lack of trained staff to provide person-centered dementia care, and residents’ families are not situated in the residents’ care circle. Four themes emerged related to the impact of current working conditions of PSWs: occupational burnout, poor resident care, frustrated and disengaged families, and PSWs leave their role. The findings offer opportunities for employers to ameliorate working conditions to support person-centered care. We conclude with specific workplace recommendations that respond to the complexity of dementia care and the associated occupational stresses PSWs experience in the current LTC environment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".