Integrating equity into hospital incident reporting and patient concerns systems: study protocol for a mixed methods study
Bibliographic record
Abstract
INTRODUCTION: Preventable hospital patient harm events disproportionally affect certain patient populations. For some, harm extends beyond physical injury to include cultural, emotional or spiritual impacts. While these disparities are linked to socio-demographics (eg, race, education), they are driven by structural factors (eg, procedures and policies). Patient safety monitoring systems (eg, incident reporting, patient concerns) were not originally designed to identify equity-related harms and may inadvertently obscure or reinforce the injustices they should address. This study will examine how equity is currently considered within hospital incident reporting and patient concerns systems across Canada and will identify opportunities to strengthen these systems' responsiveness to inequities in patient safety. METHODS AND ANALYSIS: This 3-year exploratory sequential mixed-method study began in September 2024. Phase one involves qualitative interviews with patient safety and equity leads, patients/families/caregivers and leaders of innovative initiatives to explore current practices, gaps and innovations in how equity-related factors are identified and addressed within incident reporting and patient concerns systems. Findings will inform Phase 2, a modified Delphi process with patient safety and equity experts and persons with lived experience of equity-related harm events to refine and reach consensus on key equity-promoting features, considerations and recommendations for these systems. In Phase 3, consensus items will be used to develop a national cross-sectional survey assessing the extent to which equity is integrated into hospital incident reporting and patient concerns systems in Canada. A patient advisory committee will inform data collection, interpretation of findings and dissemination. ETHICS AND DISSEMINATION: Ethics approval has been received for Phase 1, with subsequent approvals to be sought for later phases. Dissemination plans include peer-reviewed publications, presentations at international conferences and knowledge exchange activities to inform patient engagement, the design of incident reporting and patient concerns systems and policy development.
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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 | 0.095 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.101 | 0.021 |
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".