Violence, Harassment, and Turnover Intention in Home and Community Care: The Role of Training
Bibliographic record
Abstract
BACKGROUND: Violence and harassment affect healthcare workers' well-being and career decisions in the home and community care sector. PURPOSE: The objective of this study is to assess the role of training in alleviating the relationship between violence and harassment at work and turnover intention among personal support workers (PSWs). METHODOLOGY/APPROACH: Cross-sectional survey data from 1401 PSWs in Ontario, Canada are analyzed with structural equation modeling. Utilizing a resource perspective, the associations between job demands (i.e., violence and harassment at work), personal resources (i.e., self-esteem), job resources (i.e., workplace violence training and challenging task training), stress, and intention to stay among personal support workers (PSWs) are examined. RESULTS: Challenging task training is positively associated with self-esteem and negatively associated with stress, whereas workplace violence training does not have a significant association with either variable. Stress has a negative relationship with intention to stay. Self-esteem is the mediator of both associations between violence and harassment at work and stress and between challenging task training and stress. DISCUSSION: The results point to varied degrees of training effectiveness that may be shaping turnover decisions of PSWs who experience violence and harassment in home and community care organizations. PRACTICE IMPLICATIONS: There seems to be a need to assess and redesign workplace violence training. Home and community care managers might be able to lower the impact of violence and harassment on PSWs' turnover by providing training that is not directly related to workplace violence and harassment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".