Nurses' reporting across different sources and forms of workplace violence in British Columbia
Bibliographic record
Abstract
Background: Workplace Violence (WPV) in the form of physical assault, threat of assault, emotional abuse, verbal sexual harassment, and sexual assault is most often perpetrated by Type II or III sources in healthcare settings. Type II WPV refers to the perpetrator being patients, family, or visitors, whereas Type III WPV refers to the perpetrator being co-workers. Despite the frequency of these WPV forms and sources, the nature and severity of WPV cannot be fully understood due to underreporting among nurses. Purpose: This study investigated whether WPV source and perceptions of WPV prevention efforts predicted nurses’ reporting behaviours (i.e., not reporting, informal reporting, and formal reporting) of five WPV forms and reasons for not formally reporting different WPV forms and sources. Methods: A secondary analysis of 2019 WPV data from a province-wide survey in British Columbia, Canada was carried out with a sample of 4109 nurses. Multinomial logistic regression was used to analyze predictors of reporting across the five WPV forms. Descriptive statistics were used to examine the reasons for not formally reporting across WPV sources and forms. Results: Compared to nurses who only experienced Type II WPV, nurses who experienced both Type II and III WPV were less likely to informally report threat of assault, emotional abuse, and verbal sexual harassment. Although physical assault was generally more frequently informally and formally reported compared to non-physical WPV (e.g., emotional abuse), nurses who experienced physical assault from a Type III source (versus Type II source) were less likely to informally report it. Higher perceptions of WPV prevention efforts was associated with a greater likelihood of informal and formal reporting. Nurses’ reasons for not formally reporting showed some similarities and nuanced differences across WPV sources and forms. Implications: To promote reporting among nurses, healthcare organizations need effective multifaceted WPV prevention measures that include a confidential and simplified reporting system, leadership support to encourage reporting and follow-up for nurses, education and training on reporting systems and managing patient diagnoses that can contribute to WPV, and dedicated time to report WPV.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".