Bathing residents with dementia in long‐term care: critical incidents described by personal support workers
Bibliographic record
Abstract
AIM AND OBJECTIVE: The aim of this study was to describe and gain insight into the critical incidents depicted by personal support workers (PSWs) in long-term care (LTC) related to bathing residents who have dementia. BACKGROUND: Residents with dementia in LTC often display responsive/protective behaviours during bathing. Consequently, bathing is a source of stress for PSWs who provide most of the personal care for LTC residents in Ontario, Canada. DESIGN: A qualitative descriptive study employing the critical incident technique (CIT) was used. METHOD: Eight PSWs were interviewed and 24 incidents were collected and analyzed using thematic content analysis. RESULTS: Findings revealed that PSWs experience the following during bathing: managing responsive/protective behaviours, working with limited resources, and dealing with communication difficulties. Participants used various strategies to respond to these challenges; however, they reported limited strategies to manage the most challenging behaviours. CONCLUSIONS: Many of the bathing strategies described by the PSWs in this study are found in the literature about best bathing practices in dementia care. However, it is evident that further work is needed to support PSWs to manage the most difficult physical responsive/protective behaviours that occur during bathing. IMPLICATIONS FOR PRACTICE: This study has clear implications for knowledge translation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".