Anger‐provoking events and intention to turnover in hospital administrators
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to examine anger associated with types of negative work events experienced by health administrators and to examine the impact of anger on intent to leave. DESIGN/METHODOLOGY/APPROACH: Textual data analysis is used to measure anger in open-ended survey responses from administrative staff of a Canadian hospital. Multivariate regression is applied to predict anger from event type, on the one hand, and turnover intentions from anger, on the other. FINDINGS: Person-related negative events contributed to administrator anger more than policy-related events. Anger from events predicted turnover intentions after adjusting for numerous potential confounds. RESEARCH LIMITATIONS/IMPLICATIONS: Future studies using larger samples across multiple sites are needed to test the generalizability of results. PRACTICAL IMPLICATIONS: Results provide useful information for retention strategies through codifying respect and fairness in interactions and policies. Health organizations stand to gain efficiencies by helping administrators handle anger effectively, leading to more stable staffing levels and more pleasurable, productive work environments. ORIGINALITY/VALUE: This paper addresses gaps in knowledge about determinants of turnover in this population by examining the impact of administrator anger on intent to leave and the work events which give rise to anger. Given the strategic importance of health administration work and the high costs to health organizations when administrators leave, results hold particular promise for health human resources.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".