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Record W3082732243 · doi:10.1002/nop2.586

Single‐room maternity care: Systematic review and narrative synthesis

2020· review· en· W3082732243 on OpenAlexafffundabout
Elena Ali, Jill M. Norris, Marc Hall, Deborah White

Bibliographic record

VenueNursing Open · 2020
Typereview
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsCINAHLGrey literaturePatient satisfactionMEDLINENarrativeInclusion (mineral)Maternity careHealth careCochrane LibrarySystematic reviewFamily medicineMedicineNursingMeta-analysisPsychologyPsychological interventionSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Aim: To describe the single-room maternity care model and evaluate its influence on patient, provider and system outcomes. Design: Mixed-method systematic review and narrative synthesis. Methods: We conducted searches of MEDLINE, CINAHL, Web of Science, Cochrane Database of Systematic Reviews, and the grey literature from January 1985-August 2018, yielding 151 records. Pairs of reviewers independently applied the inclusion criteria using a standardized screening tool to both titles/abstracts and full texts. Overall, 13 studies were retained. Results: Most studies of single-room care were from the United States and Canada, and assessed costs, patient satisfaction and/or provider satisfaction. Studies used cross-sectional and/or pre-post comparative, retrospective descriptive and qualitative designs. Methodological quality of quantitative studies was generally weak, and few studies conducted inferential statistics. Maternal satisfaction with the single-room maternity model was positive across the studies; however, healthcare provider satisfaction was mixed.

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)0.042
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.042
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.142
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0110.008
Bibliometrics0.0180.017
Science and technology studies0.0010.002
Scholarly communication0.0050.006
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.124
GPT teacher head0.446
Teacher spread0.323 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations14
Published2020
Admission routes3
Has abstractyes

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