Emerging Issues and Challenges for Improving Patient Safety in Mental Health
Bibliographic record
Abstract
OBJECTIVES: It is only recently that patient safety in mental health was considered a field in its own right, and there is a lack of awareness of the issues and a shortage of readily available information. This research builds on existing knowledge by soliciting the expertise of leaders in the area of patient safety and/or mental health via 2 qualitative methods. METHODS: Qualitative interviews were held with 19 key informants. Small group discussions were held during a Canadian invitational roundtable event with 72 participants. A thematic qualitative analysis involving a 2-step process was performed: (1) coding each interview, and (2) identifying larger themes. RESULTS: The findings revealed that more work is required to establish clear patient safety definitions, develop awareness, set priorities, and develop strategies for responding to patient safety incidents in mental health settings. Establishing a culture of patient safety and embedding it within all levels of an organization is vital, including adopting a systems level approach to examining patient safety incidents, encouraging open reporting and communication, considering the patient/caregiver perspective, and eliminating discrimination and stigma. Patient safety issues pertaining to community care settings are an urgent issue and require greater understanding. The need to promote national leadership, standardization of practice, ongoing training, information sharing, and additional research also was voiced. CONCLUSIONS: The results from this research highlight that greater action is required to improve patient safety in mental health settings. This research has identified several potentially important future directions for improving patient safety in mental health.
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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 | 0.077 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".