941 The implementation of violence prevention policies and programs in hospitals
Bibliographic record
Abstract
Introduction Violence in hospitals is a serious occupational health and safety (OHS) issue affecting the physical and mental health of front line staff, as well as, the quality of patient care. In 2010, the province of Ontario (Canada) introduced legislation that directs hospitals to put into place violence prevention and management systems. Our study examined how five Ontario hospitals have developed and implemented their violence prevention programs. Methods Semi structured interviews were conducted with eight key informants external to hospitals (legislators, union leaders, hospital associations), management and occupational health and safety specialists in hospitals (n=40), 21 focus groups (n=115) and interviews (n=6) with front line workers. Five hospitals participated in the study. Interview and focus group questions focused on the effect of the legislation on the development of violence prevention programs and how these were implemented across departments. Once data were collected, a code list was developed by the research team by reviewing the transcripts. Each transcript was coded by two researchers and then a thematic, inductive analysis was carried out. The constant comparative method was used to identify differences and similarities across hospitals and to understand factors that shape hospital policies and practices in the area of violence prevention and management. Findings Our study findings suggest that while legislation sets parameters for the development of policies, serious violence-related events and the presence of a violence prevention ‘champion’ bolster long-term commitment to violence prevention in hospitals and the development of sustainable programs. We discuss four key components related to the prevention and management of violence in hospitals, namely; security systems, patient ‘flagging’, codes and alarms and incident reporting. Discussion Our findings detail how management commitment, workplace culture and broader structural factors can shape the implementation of hospital policies around violence prevention and reporting. Study recommendations focus on the long-term sustainability of violence prevention practices in the acute care sector and the implications this can have on worker health.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".