What near misses tell us about risk and safety in mental health care
Bibliographic record
Abstract
Accessible summary How near misses in the mental health sector are experienced is not well understood. Study findings elucidate the nature of near misses as both (1) vulnerabilities and risk associated with the mental health population (e.g. violence, aggression, fear and error proneness); and (2) ‘no‐harm events’ where clinicians or service users minimize or prevent harm from happening. Study findings have implications for practice, education, research and policy associated with recognizing and responding to safety threats in a timely manner to prevent harm to service users and providers. Abstract How service providers and service users view near misses in their daily practice within the rubric of patient safety events is not well understood. Further no studies were located that explored near misses specifically in mental health settings in Canada. In this context, a qualitative study was undertaken to gain insight into how service providers and service users (mental health clients or their family members) experienced and defined near misses. Eight (8) focus groups (n= 88) with service providers and 28 semi‐structured interviews with service users were conducted at three mental health care organizations. Content analysis was employed to the dataset that elucidated that near misses were (1) safety threats and vulnerabilities associated with experiencing mental illness; and (2) acts that avert harm and prevent something from happening. Findings are compared to what is currently known about in safety. Implications of findings for practice, research and policy are delineated.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".