Work Mistreatment and Hospital Administrative Staff: Policy Implications for Healthier Workplaces
Bibliographic record
Abstract
Research on work life quality in hospitals has focused on how nurses and physicians perceive or react to work conditions. We extend this focus to another major professional group - healthcare administrators - to learn more about how these employees experience the work environment. Administrators merit such attention given their key roles in sustaining the financial health of the hospital and in fulfilling management functions efficiently to support consistent, high-quality care. Specifically, we examined mistreatment in the workplace experienced by administrative staff from a hospital in a large Canadian city. Three dimensions of mistreatment - verbal abuse, work obstruction and emotional neglect - have been associated with diminished well-being, work satisfaction and organizational commitment, along with stronger intent to leave. In this paper, we provide additional support for interpreting these three dimensions as mistreatment and report on their frequencies in our sample. We then consider implications for policy development (e.g., communication and conflict resolution skills training, mentoring programs, respect-at-work policies) to make workplaces healthier for these neglected but important healthcare professionals.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".