Experiencing violence in a psychiatric setting: Generalized hypervigilance and the influence of caring in the fear experienced
Bibliographic record
Abstract
BACKGROUND: Exposure to violence in the mental health sector both affects employees and has implications for the quality of care provided. OBJECTIVE: This phenomenological study aims to describe and understand the ways in which acts of aggression from a patient might affect workers in a psychiatric institute, their relationships with the patients and the services offered. METHODS: Two semi-structured interviews were conducted with each of the 15 participants from various professions within a psychiatric hospital. RESULTS: Our analysis reveals four themes: hypervigilance, caring, specific fear toward the aggressor and generalized fear of all patients. A state of hypervigilance is found among all participants. An emphasis on caring is present among the majority and unfolds as a continuum, ranging from being highly caring to showing little or no caring. A feeling of fear is expressed and is influenced by the participant's place on the caring continuum. Caring workers developed a specific fear of their aggressor, whereas those showing little or no caring developed a generalized fear of all patients. Following a violent event, caring participants maintained this outlook, whereas those demonstrating little to no caring were more inclined to disinvest from all patients. CONCLUSIONS: Hypervigilance and fear caused by experiences of violence impact the quality of care provided. Considerable interest should thus be paid to caring, which can influence fear and its effects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".