The role of aggressions suffered by healthcare workers as predictors of burnout
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To examine the prevalence of aggression against healthcare professionals and to determine the possible impact that violent episodes have on healthcare professionals in terms of loss of enthusiasm and involvement towards work. The objective was to analyse the percentage of occupational assault against professionals' aggression in different types of healthcare services, differentiating between physical and verbal aggression as a possible variable in detecting burnout in doctors and nursing professionals. BACKGROUND: Leiter and Maslach have explored a double process model of burnout not only based on exhaustion by overload, but also based on personal and organisational value conflicts (community, rewards or values). Moreover, Whittington has obtained conclusive results about the possible relationship between violence and burnout in mental health nurses. DESIGN: A retrospective study was performed in three hospitals and 22 primary care centres in Spain (n = 1·826). METHODS: Through different questionnaires, we have explored the relationship between aggression suffered by healthcare workers and burnout. RESULTS: Eleven percent of respondents had been physically assaulted on at least one occasion, whilst 34·4% had suffered threats and intimidation on at least one occasion and 36·6% had been subjected to insults. Both forms of violence, physical and non-physical aggression, showed significant correlations with symptoms of burnout (emotional exhaustion, depersonalisation and inefficacy). CONCLUSIONS: The survey showed evidence of a double process: (1) by which excess workload helps predict burnout, and (2) by which a mismatch in the congruence of values, or interpersonal conflict, contributes in a meaningful way to each of the dimensions of burnout, adding overhead to the process of exhaustion-cynicism-lack of realisation. Relevance to clinical practice. Studies indicate that health professionals are some of the most exposed to disorders steaming from psychosocial risks and a high comorbidity: anxiety, depression, etc. There is a clear need for accurate instruments of evaluation to detect not only the burnout but also the areas that cause it. Professional exhaustion caused by aggression or other factors can reflect a deterioration in the healthcare relationship.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".