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Record W4309665600 · doi:10.1891/cn-2022-0050

Obstetric Nurses’ Self-Efficacy, Demographic Characteristics, and Family-Focused Care during Simulated Events

2022· article· en· W4309665600 on OpenAlexaff
Sabrina Ehmke, Stacey Van Gelderen, Marilyn A. Swan, Laura Bourdeanu

Bibliographic record

VenueCreative Nursing · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsAmerican Water (Canada)
Fundersnot available
KeywordsRubricCertificationSpecialtyNursingMedicineFamily medicineSelf-efficacyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Introduction/Background: A study involving 48 obstetric nurses explored the relationship between self-efficacy scores and demographic variables, and family-focused care during obstetrical emergencies. Methods: Obstetric Nursing Self-Efficacy Scale scores and demographic data were collected, and the Van Gelderen Family Care Rubric (VGFCR) was administered following simulation of obstetrical emergencies. Results: Two variables were found to influence the VGFCR scores. Nursing specialty certification and previous education in family-focused care. Conclusion: Improvements in the delivery of family-focused care can be achieved with simulation education and nursing specialty certification achievement.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.352
Teacher spread0.309 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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