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Record W3016870218 · doi:10.1136/bmjopen-2019-036203

Obstetrical safety indicators for preventing hospital harms in low risk births: a scoping review protocol

2020· review· en· W3016870218 on OpenAlexafffund
Aislinn Conway, Jessica Reszel, Mark Walker, Jeremy Grimshaw, Sandra Dunn

Bibliographic record

VenueBMJ Open · 2020
Typereview
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsOttawa HospitalChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersCanadian Institutes of Health ResearchInstitute of Health Services and Policy ResearchCHEO Research Institute
KeywordsCINAHLMedicineThematic analysisMEDLINEHarmProtocol (science)Patient safetyInclusion (mineral)Cochrane LibraryHealth careDocumentationData collectionGrey literatureHealth informaticsNursingQualitative researchMedical emergencyPublic healthAlternative medicinePsychological interventionPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.143
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.143
Threshold uncertainty score0.754

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1430.142
Meta-epidemiology (narrow)0.0060.007
Meta-epidemiology (broad)0.0110.015
Bibliometrics0.0210.016
Science and technology studies0.0060.007
Scholarly communication0.0100.011
Open science0.0080.009
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0840.021

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.

Opus teacher head0.245
GPT teacher head0.603
Teacher spread0.358 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations4
Published2020
Admission routes2
Has abstractyes

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