A concept analysis of women's vulnerability during pregnancy, birth and the postnatal period
Bibliographic record
Abstract
AIM: To report an analysis of the concept of vulnerability associated with pregnancy, birth and the postnatal period. BACKGROUND: The concept of vulnerability during childbirth is complex and the term, 'to be vulnerable' frequently attains a vague application. Analysis about vulnerability is needed to guide policy, practice, education and research. Clarity around the concept has the potential to improve outcomes for women. DESIGN: Concept analysis. DATA SOURCES: Searches were conducted in CINAHL, EMBASE, PubMed, Psychinfo, MEDLINE, MIDIRS and ASSIA and limited to between January 2000 - June 2014. Data were collected over 12 months during 2014. METHODS: This concept analysis drew on Morse's qualitative methods. RESULTS: Vulnerability during pregnancy, birth and the postnatal period can be defined by three main attributes: (a) Threat; (b) Barrier; and (c) Repair. Key attributes have the potential to influence outcome for women. Inseparable sub-attributes such as mother and baby attachment, the woman's free will and choice added a level of complexity about the concept. CONCLUSION: This concept analysis has clarified how the term vulnerability is currently understood and used in relation to pregnancy, birth and the postnatal period. Vulnerability should be viewed as a complex phenomenon rather than a singular concept. A 'vulnerability journey plan' has the potential to identify how reparative interventions may develop the woman's capacity for resilience and influence the degree of vulnerability experienced. Methodology based around complex theory should be explored in future work about vulnerability.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| 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".