Integrating equity into incident reporting and patient concerns systems: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Hospital incident reporting and patient concerns systems are widely used to detect and respond to patient harm. Despite increasing recognition of the link between equity and safety, equity remains poorly integrated into the design and function of these systems. Consequently, these systems risk obscuring or reproducing inequities rather than revealing and attending to them. OBJECTIVE: To examine how issues of equity are currently considered in research about hospital incident reporting and patient concerns systems and identify opportunities to more systematically include equity in how patient safety is addressed. METHODS: A critical interpretive synthesis was conducted to develop a theoretical understanding of the topic through inductive analysis and interpretation. The databases CINAHL, EMBASE, MEDLINE and PsycINFO were searched from database inception to 6 February 2024. Select social science, patient safety and health services literature supported the interpretive process. RESULTS: After screening 6508 abstracts and conducting hand searches, we included 30 articles in our review. Our analysis identified four equity-related themes. The first theme describes how knowledge injustices in 'what counts as a safety event or contributor' shape what patient issues are recognised, recorded and addressed. The second theme examines how individual bias and systemic discrimination affect which safety events and concerns get reported. The third theme explores both opportunities and limitations of stratifying data to uncover equity-related patterns of harm. The fourth theme presents alternate frameworks, including restorative and human rights approaches, as ways to address inequities and humanise harm. CONCLUSION: The findings provide direction for changes within incident reporting and patient concerns practices (eg, expanding definitions of harms; creating accessible and culturally safe patient concerns systems). They also affirm the opportunity to learn from, and build on, initiatives such as taking a restorative approach that moves beyond a customer service and risk management framing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".