Patient and Family Involvement in Serious Incident Investigations From the Perspectives of Key Stakeholders: A Review of the Qualitative Evidence
Bibliographic record
Abstract
OBJECTIVES: Investigations of healthcare harm often overlook the valuable insights of patients and families. Our review aimed to explore the perspectives of key stakeholders when patients and families were involved in serious incident investigations. METHODS: The authors searched three databases (Medline, PsycInfo, and CINAHL) and Connected Papers software for qualitative studies in which patients and families were involved in serious incident investigations until no new articles were found. RESULTS: Twenty-seven papers were eligible. The perspectives of patients and families, healthcare professionals, nonclinical staff, and legal staff were sought across acute, mental health and maternity settings. Most patients and families valued being involved; however, it was important that investigations were flexible and sensitive to both clinical and emotional aspects of care to avoid compounding harm. This included the following: early active listening with empathy for trauma, sincere and timely apology, fostering trust and transparency, making realistic timelines clear, and establishing effective nonadversarial communication. Most staff perceived that patient and family involvement could improve investigation quality, promote an open culture, and help ensure future safety. However, it was made difficult when multidisciplinary input was absent, workload and staff turnover were high, training and support needs were unmet, and fears surrounded litigation. Potential solutions included enhancing the clarity of roles and responsibilities, adequately training staff, and providing long and short-term support to stakeholders. CONCLUSIONS: Our review provides insights to ensure patient and family involvement in serious incident investigations considers both clinical and emotional aspects of care, is meaningful for all key stakeholders, and avoids compounding harm. However, significant gaps in the literature remain.
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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 | 0.086 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".