Evidence and compassion in Maternity care / C.S.Minnie
Bibliographic record
Abstract
Maternity care is the care of the mother-baby dyad during pregnancy, childbirth and the first few months thereafter. Various members of the multidisciplinary team can offer maternity care, and the focus of my research is on the role of the midwife, who is the ‘specialist’ in physiological childbirth. \nA “good midwife” is competent in the cognitive (mind), psychomotor (hands) and affective (heart) domains. \nMy research focuses on the overlap between evidence-based practice (mind and hands) and compassion (heart) to ensure quality maternity care. Evidence-based practice (using the best available evidence in practice) can be considered as the combination of the mind and the hands. \nMy interest in evidence-based practice includes systematic (and other research -) review methodology and implementation science (the process of how primary research is synthesised, transferred/ translated) eventually implemented in practice). I collaborated with researchers from Canada and Europe and presented workshops, and published a few book chapters and articles on these aspects. \nCompanionship during childbirth is both a specific evidence-based practice and a component of compassionate maternity care. A few studies investigated aspects of companionship: how women experience continuous support during childbirth, the challenges of midwives in implementing continuous support and how midwives can facilitate such support even if they cannot offer it themselves. Two PhD students are busy developing a strategy to facilitate the implementation of continuous support during childbirth and a program to promote supportive interpersonal relationships between midwives, women and their companions. \nMy research on compassionate maternity care addresses the affective domain of a midwife’s work (the heart). Two concepts received a lot of emphases globally in the last ten years, namely ‘Disrespect and Abuse during childbirth’ and ‘Respectful maternity care’. My research focuses on building on what works - an appreciative approach and therefore concentrate more on respectful or compassionate maternity care. Individual studies investigated the characteristics of compassionate maternity care from the perspectives of midwives and new mothers. I am also interested in assessing compassionate maternity care offered in maternity units. A current project in my research program is an integrative literature review to identify instruments that can be used as-is or adapted for the South African context. The next step will be to develop a strategy to promote compassionate maternity care and then implement and evaluate it. \nMy research program aims to improve the care of mothers and their infants. My research program investigates related aspects, and studies build on each other to ultimately benefit mothers, their infants and society.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".