Bibliographic record
Abstract
INTRODUCTION: Evidence supports numerous positive clinical benefits of doula care. There are varying attitudes among physicians, midwives, and nurses toward support of doulas in a collaborative approach with women in labor. Tension and conflict with use of doulas may occur in some intrapartum settings in the United States. METHODS: A scoping review of the literature between January 2008 and January 2018 was conducted using PubMed, CINAHL, Google Scholar, and Scopus database to identify specific attitudes of physicians, midwives, and nurses toward doulas; 1,810 records were identified and initially reviewed. Inclusion criteria included original research published in the last 10 years and in the English language. Articles were excluded if the research was not original and if obstetrical providers' or nurses' attitudes toward doulas were not included. RESULTS: Three records met criteria for inclusion. All used a cross-sectional survey design. Two were set in Canada exclusively and one was inclusive of nurses and doulas in both Canada and the United States. Themes emerged that may explain the influence and variances in attitudes toward doulas and the support they provide to laboring women. CLINICAL IMPLICATIONS: More research is needed to identify attitudes of members of the maternity care team toward doulas and to better understand implications of their attitudes on working together collaboratively and on patient outcomes.
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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 | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".