STAFF PERSPECTIVES ON COUNTERING STAFF-TO-RESIDENT MISTREATMENT IN LONG-TERM CARE FACILITIES
Bibliographic record
Abstract
Abstract In Canada, as well as in other countries, resident mistreatment is common in long-term care (LTC) facilities. In many situations, residents are mistreated by LTC staff. To address this problem, LTC facility managers and their employees must play an active role in the prevention as well as in the management of staff-to-resident mistreatment situations. However, it is still unclear what type of support they need to counter this type of mistreatment. Using an exploratory descriptive qualitative design, twenty-one managers and employees working in four different LTC facilities participated in semi-structured individual interviews. To allow participants to express themselves without risking self-incrimination or feeling pressured to report colleagues, vignettes depicting fictitious and common situations of staff-to-resident mistreatment were used as a conversation starter. Data analysis was performed using Miles, Huberman & Saldaña (2013) analytical method. Results show that participants think that staff-to-resident mistreatment is mainly caused by three staff characteristics: 1) not having the psychological profile to work in LTC facilities; 2) lack of training; and/or 3) being overworked. Consequently, participants believe that mistreatment prevention starts by improving employee selection practices to ensure candidates have adequate attitudes and training to work in LTC facilities. They also argue that staff should receive more training regarding mistreatment. Lastly, support interventions are suggested to prevent and address situations involving staff experiencing high levels of stress for personal or work-related reasons. This study shows that both individual and organisational measures are needed to fight against staff-to-resident mistreatment in LTC facilities.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".