Families of victims of homicide: qualitative study of their experiences with mental health inquiries
Bibliographic record
Abstract
BACKGROUND: Investigations may be undertaken into mental healthcare related homicides to ascertain if lessons can be learned to prevent the chance of recurrence. Families of victims are variably involved in serious incident reviews. Their perspectives on the inquiry process have rarely been studied. AIMS: To explore the experiences of investigative processes from the perspectives of family members of homicide victims killed by a mental health patient to better inform the process of conducting inquiries. METHOD: The study design was informed by interpretive description methodology. Semi-structured interviews were conducted with five families whose loved one had been killed by a mental health patient and where there had been a subsequent inquiry process in New Zealand. Data were analysed using an inductive approach. RESULTS: Families in this study felt excluded, marginalised and disempowered by mental health inquires. The data highlight these families' perspectives, particularly on the importance of a clear process of inquiry, and of actions by healthcare providers that indicate restorative intent. CONCLUSIONS: Families in this study were united in reporting that they felt excluded from mental health inquiries. We suggest that the inclusion of families' perspectives should be a key consideration in the conduct of mental health inquiries. There may be benefit from inquiries that communicate a clear process of investigation that reflects restorative intent, acknowledges victims, provides appropriate apologies and gives families opportunities to contribute.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".