Mapping of scientific literature on respectful maternity care and obstetric violence
Bibliographic record
Abstract
Disrespectful and abusive care in public healthcare facilities is one of the main reasons that women tend to deny access public health care facilities. This study tries to appraise the present trends of research on respectful maternity care in public health care facilities around the world. Bibliometric analysis of research articles published during 2013-2023 and indexed in PubMed was done to this purpose. The Dimension database was used to extract PubMed data and the VosViewer was used for the data visualization. The highest number of 104 research articles respectful maternity care got published in 2022. Articles published in 2018 got the highest number of 1649 citations followed by 1342 in 2019, 1325 in 2016, 1236 in 2017, 1037 in 2020, and 883 in 2015. Among the top ten most influential countries the United States topped the list with 117 publications, followed by United Kingdom, Ethiopia, Australia, Canada, jointly Ghana and Tanzania, Netherlands, Switzerland and Sweden with 54, 45, 42, 31, 19, 13 and 11 articles respectively. The World Health Organizations (WHO) had been the most influential organization with 1738 citations for only 12 published articles. It had the huge citation impact of 144.83%. WHO was followed by London School of Hygiene & Tropical Medicine, Harvard University and London School of Hygiene & Tropical Medicine with citation impact of 99.8%, 89.56% and 81.65% respectively.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.110 | 0.123 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".