Making sense of violence and victimization in health care work: The emotional labour of ‘not taking it personally’
Bibliographic record
Abstract
Despite significant impacts on employee health, workplace violence tends to be minimized and normalized by service workers and by organizations, with employees implicitly held culpable for causing aggression through how they manage interactions. Little is known about how workers accomplish minimization and normalization, or how this process might be entwined with the emotional labour of containing difficult emotions. In this paper an emotional labour lens is joined with a social phenomenological approach to analyze in-depth interviews with 26 employees of one multi-unit health care facility in Western Canada. The purpose was to examine health care workers’ emotional and interpretive responses to aggression from patients and families. Through undertaking ‘deep acting’ and maintaining their moral identities, workers contained fear through minimizing and normalizing aggression and contained frustration through acknowledging mitigating circumstances. This involved constructing themselves as victims of misdirected emotions, and patients and families as victims of aging, caregiving, disability, dementia, and/or dying processes. Emotional labour supports organizational interests in ensuring smooth workflows and promoting patient satisfaction and well-being. It involves ambivalence and contradiction and can reproduce discourses detrimental both to workers and to resident care.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".