Obstetrical safety indicators for preventing hospital harms: a scoping review protocol
Bibliographic record
Abstract
Introduction: Optimizing the safety of obstetric patient care is a primary concern for many hospitals. Identifying performance indicators that measure aspects of patient care processes related to preventable harms can present opportunities to improve health systems. In this paper, we present our protocol for a scoping review to identify performance indicators for obstetric safety. We aim to identify a comprehensive list of obstetric safety indicators which may help reduce the number of preventable patient harms, to summarize the data and to synthesize the results. Methods and analysis: We will use the methodological framework described by Arksey and O Malley and further expanded by Levac. We will search multiple electronic databases such as Medline, Embase, CINAHL and the Cochrane Library as well as websites from professional bodies and other organisations, using an iterative search strategy. We will include indicators that relate to preventable harms in the process of obstetric care. Two reviewers will independently screen titles and abstracts of search results to determine eligibility for inclusion. For records where eligibility is not clear, the reviewers will screen the full text version. If reviewers decisions regarding eligibility differ, a third reviewer will review the full text record. Two reviewers will independently extract data from records that meet our inclusion criteria using a standardized data collection form. We will narratively describe quantitative data, such as the frequency with which indicators are identified, and conduct a thematic analysis of the qualitative data. We will compile a comprehensive list of patient safety indicators identified during our scoping review and organise them according to concepts that best suit the data such as the Donabedian model or the Hospital Harm Framework. We will discuss the implications of the indicators for future research, clinical practice and policy making. We will report the conduct of the review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA ScR) Checklist. Ethics and Dissemination: The sources of information included in this scoping review will be available to the public. Therefore, ethical review for this research is not warranted. We will disseminate our research results using multiple modes of delivery such as a peer-reviewed publication, conference presentations and stakeholder communications. Keywords: Obstetrics, patient safety, performance indicators, prevention, hospital harms, scoping review, protocol.
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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 | 0.176 | 0.162 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.076 | 0.020 |
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".