Antecedents of customer aggressive behavior against healthcare employees
Bibliographic record
Abstract
Purpose The purpose of the research was to develop a tool for measuring antecedents of customer aggressive behavior (CAB) in healthcare service settings, by identifying its roots in organizational and interpersonal dynamics. Design/methodology/approach Four studies were conducted. In Studies 1 and 2, antecedents of CAB were identified through analysis of internet reader comments and a questionnaire was distributed to students. In Study 3, scenarios were used to validate the findings of the previous studies. Finally, in Study 4, a scale was developed and validated for measuring organization- and person-related triggers of CAB using samples of 477 employees and 579 customers. Findings The concept of CAB was conceptualized and validated. In total, 18 items were identified across five dimensions: personal characteristics, uncomfortable environment, aggressive role models, reinforcement of aggressive behavior and aversive treatment. The scale demonstrated good psychometric results. Research limitations/implications The research relies mainly on customer perspective. Employees and additional stakeholders should be included to achieve more accurate information that could contribute to a better understanding of CAB and its roots. Practical implications Exploring social and organizational antecedents that trigger CAB could help healthcare managers evaluate and proactively manage CAB and its implications within their organization. Originality/value This measurement scale is the first comprehensive tool, based on Bandura’s social learning theory (1973), that may identify and measure antecedents of CAB, and could be used to reduce CAB in healthcare service settings.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".