Applying an equity lens to hospital safety monitoring: a critical interpretive synthesis protocol
Bibliographic record
Abstract
INTRODUCTION: Hospital safety monitoring systems are foundational to how adverse events are identified and addressed. They are well positioned to bring equity-related safety issues to the forefront for action. However, there is uncertainty about how they have been, and can be, used to achieve this goal. We will undertake a critical interpretive synthesis (CIS) to examine how equity is integrated into hospital safety monitoring systems. METHODS AND ANALYSIS: This review will follow CIS principles. Our initial compass question is: How is equity integrated into safety monitoring systems? We will begin with a structured search strategy of hospital safety monitoring systems in CINAHL, EMBASE, MEDLINE and PsycINFO for up to May 2023 to identify papers on safety monitoring systems generally and those linked to equity (eg, racism, social determinants of health). We will also review reference lists of selected papers, contact experts and draw on team expertise. For subsequent literature searching stages, we will use team expertise and expert contacts to purposively search the social science, humanities and health services research literature to support the development of a theoretical understanding of our topic. Following data extraction, we will use interpretive processes to develop themes and a critique of the literature. The above processes of question formulation, article search and selection, data extraction, and critique and synthesis will be iterative and interactive with the goal to develop a theoretical understanding of equity in hospital monitoring systems that will have practice-based implications. ETHICS AND DISSEMINATION: This review does not require ethical approval because we are reviewing published literature. We aim to publish findings in a peer-reviewed journal and present at conferences.
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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 | 0.256 | 0.432 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.030 | 0.025 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.084 | 0.012 |
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".