Maternity care during COVID-19: a protocol for a qualitative evidence synthesis of women’s and maternity care providers’ views and experiences.
Bibliographic record
Abstract
Background: Considerable changes in maternity care provision internationally were implemented in response to COVID-19. Such changes, often occurring suddenly with little advance warning, have had the potential to affect women’s and maternity care providers experience of maternity care, both positively and negatively. For this reason, to gain insight and understanding of personal and professional experiences, we will perform a synthesis of the available qualitative evidence on women and maternity care providers’ views and experiences of maternity care during COVID-19. Methods and analysis : A qualitative evidence synthesis will be conducted. Studies will be eligible if they include pregnant or postpartum women (up to six months) and maternity care providers who received or provided care during COVID-19. To retrieve relevant literature the electronic databases of CINAHL, EMBASE, MEDLINE, PsycINFO, and the Cochrane COVID study register ( https://covid-19.cochrane.org/ ) will be searched from 01-Jan-2020 to date of search. A combination of search terms based on COVID-19, pregnancy, childbirth and maternity care, and study design, will be used to guide the search. The methodological quality of the included studies will be assessed by at least two reviewers using the Evidence for Policy and Practice Information (EPPI)-Centre 12-criteria quality assessment tool. The Thomas and Harden approach to thematic synthesis will be used for data synthesis. This will involve line by line coding of extracted data, establishing descriptive themes, and determining analytical themes. Confidence in the findings of the review will be assessed by two reviewers independently using Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual). Conclusion : The proposed synthesis of evidence will help identify maternity care needs during a global pandemic from the perspectives of those receiving and providing care. The evidence will inform and help enhance care provision into the future.
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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 | 0.167 | 0.135 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.143 | 0.023 |
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".