Unveiling the veil: Exploring experiences of patient-initiated workplace violence in long-term care and mental health and substance use settings
Bibliographic record
Abstract
BackgroundThis paper focuses on patient-initiated workplace violence (referred to WPV hereafter) towards healthcare workers in long-term care (LTC) and mental health and substance use (MHSU) care settings. While an extensive body of evidence has thoroughly examined WPV, our understanding of what takes place immediately before or during a violent incident, known as 'on-the-spot' experiences is limited.ObjectiveThis study examined (a) 'on-the-spot' experiences, (b) contributing factors, and (c) warning signs of impending WPV using the experiences of healthcare worker victims and witnesses and healthcare attendees in LTC and MHSU.MethodsThe study was conducted in one LTC home and two MHSU units in British Columbia, Canada. In-depth semi-structured virtual interviews were conducted with 16 participants from June to September 2023. Workplace Health Indicator Tracking and Evaluation (WHITE) data included 38 WPV incidents occurring between January 2022 to March 2023. Data were analyzed using thematic analysis.ResultsSix participants (35%) identified as both victims and witnesses of WPV, four participants (24%) as only victims, five participants (29%) as only witnesses, and one participant (6%) as neither a victim nor a witness." Contributing factors to WPV encompassed two main themes and their subthemes: (1) patient/resident factors (cognitive impairment and neurodevelopmental conditions); (2) healthcare factors (lack of continuity of care across healthcare, community and family, care provision, approaches and skills in interactions with patients/residents, access to safety tools and security personnel, and unmet needs and workload and human resource challenges).ConclusionWPV may be reduced through access to specialized care, adoption of team-based care and person-centered care approaches, addressing resource constraints, and offering context-specific training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".