Work stress and physical assault of nursing aides in rural nursing homes with and without dementia special care units
Bibliographic record
Abstract
PURPOSE: This study compared nursing aides (NAs) employed in rural nursing homes with and without dementia special care units (SCUs) on (1) exposure to and distress from disruptive behaviours exhibited by residents, (2) job strain and (3) physical assault. DESIGN AND METHODS: The data were drawn from a larger study conducted in Saskatchewan, Canada, in which all rural nursing homes of < or = 100 beds that had an SCU were matched to same-sized rural facilities with no SCU. Nursing aides (n = 355) completed a mailed survey questionnaire. RESULTS: Nursing aides employed in nursing homes with an SCU reported significantly less frequent exposure to disruptive behaviours (including aggressive and aversive behaviours) than NAs in non-SCU facilities, less distress when these behaviours were directed toward them, less exposure to aggressive behaviour during caregiving, lower job demands and lower job strain. There was a trend toward increased risk of being assaulted in the last year associated with being in a non-SCU facility. Having a permanent position, increased job strain, and feeling inadequately prepared for dementia care were significantly associated with higher risk of being assaulted. In the SCU facilities, NAs who worked more time on the SCU reported more assaults but less distress from disruptive behaviour, lower psychological job demands, lower job strain and greater work autonomy. IMPLICATIONS: Providing more dementia care training and reducing job demands and job strain may help to reduce work-related stress and physical assault of nursing aides employed in nursing homes.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".