Competencies for respectful maternity care: Identifying those most important to midwives worldwide
Bibliographic record
Abstract
BACKGROUND: A respectful, person-centered philosophy of maternity care has been emerging over several decades. Research conducted on behalf of the International Confederation of Midwives (ICM) to identify essential competencies for midwifery practice also identified the knowledge, skills, and professional behaviors that should be hallmarks of respectful maternity care practices among the global community of midwives. METHODS: A three-round, online, modified Delphi survey was conducted between April 2016 and October 2016. A total of 895 individuals from 90 of the then-current 105 ICM member countries participated, with good representation across English, French, and Spanish speakers, high-income, medium-income, and low-income countries, and educators and clinicians. RESULTS: A total of 115 respectful maternity care (RMC)-related items were endorsed by participants in Round 1 or 2. These items received average scores of between 90.24% and 99.10%, well above the 85% threshold required to be identified as within the scope of global midwifery practice. These items were compared with the 12 domains of RMC identified by Shakibazadeh and colleagues that defined respectful care during childbirth in health facilities globally, and with similar RMC frameworks, and were found to be highly congruent, thus demonstrating the high value of RMC within the core of midwifery practice. DISCUSSION: ICM survey items were endorsed across all 12 RMC domains proposed by Shakibazadeh et al, and the findings affirmed that across ICM countries and regions, the philosophy of RMC was integrally related to the knowledge, skills, and professional behaviors that emerged as essential for basic midwifery practice.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".