Midwifery Care: An Evolutionary Concept Analysis
Bibliographic record
Abstract
AIM: To build an evidence-based definition of midwifery care and its fundamental and distinctive features. DESIGN: Rodgers and Knafl's evolutionary concept analysis. REVIEW METHODS: Six databases (PubMed, CINAHL, Web of Science, PsycINFO, Scopus, ProQuest) were searched and a thematic sampling of the sources was performed. DATA SOURCES: The search yielded 30 relevant papers. RESULTS: Key findings include five antecedent categories: philosophy, personal features, regulatory features, care context and professional team. Attributes include relationship and family-centredness. Consequences encompass safety, empowerment and professional outcomes. Related concepts and surrogate terms reflect the broader scope and the fragmented perception of 'midwifery care'. DISCUSSION: Midwifery care is often limited by obstetric-led models that prioritise risk management over holistic care. This disparity leads to discrimination and professional dissatisfaction, impacting on the quality of care and midwives' well-being. IMPLICATIONS FOR THE PROFESSION: Through the conceptualisation of midwifery care, research, education, clinical practice and governance can be oriented toward professional priorities, enhancing coherence, awareness and relevance in relation to the ontological nature of the profession and its systemic value within healthcare and society. In this context, it is crucial for policymakers to maximise the development and implementation of policies that support the establishment of care models based on the principles of midwifery care, ensuring a more comprehensive and effective healthcare system. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.051 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".