Obstetrical safety indicators for preventing hospital harms in low risk births: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Optimising the safety of obstetric patient care is a primary concern for many hospitals. Performance indicators measuring aspects of patient care processes can lead to improvements in health systems and the prevention of harm to the patient. We present our protocol for a scoping review to identify indicators for obstetric safety in low risk births. We aim to identify indicators addressing preventable hospital harms, to summarise the data and synthesise results. METHODS AND ANALYSIS: . We will search electronic databases such as Medline, Embase, CINAHL and the Cochrane Library, and websites from professional bodies and other organisations, using an iterative search strategy.Two reviewers will independently screen titles and abstracts of search results to determine eligibility for inclusion. If eligibility is not clear, the reviewers will screen the full text version. If reviewers' decisions regarding eligibility differ, a third reviewer will review the record. Two reviewers will independently extract data from records that meet our inclusion criteria using a standardised 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 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 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 checklist. ETHICS AND DISSEMINATION: The sources of information included in this scoping review will be available to the public. Therefore, ethics approval is not warranted. We will disseminate results in a peer-reviewed publication, conference/event presentation(s) and stakeholder communications.
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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 | 0.143 | 0.142 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.015 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.084 | 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".