Why do some pregnant women not fully disclose at comprehensive psychosocial assessment with their midwife?
Bibliographic record
Abstract
PROBLEM: While comprehensive psychosocial assessment is recommended as part of routine maternity care, unless women engage and disclose, psychosocial risk will not be identified or referred in a timely manner. We need to better understand and where possible overcome the barriers to disclosure if we are to reduce mental health morbidity and complex psychosocial adversity. AIMS: To assess pregnant women's attitude to, and reasons for non-disclosure at, comprehensive psychosocial assessment with their midwife. METHODS: Data from 1796 pregnant women were analysed using a mixed method approach. After ascertaining women's comfort with, attitude to, and non-disclosure at psychosocial screening, thematic analysis was used to understand the reasons underpinning non-disclosure. FINDINGS: 99% of participants were comfortable with the assessment, however 11.1% (N = 193) reported some level of nondisclosure. Key themes for non-disclosure included (1) Normalising and negative self-perception, (2) Fear of negative perceptions from others, (3) Lack of trust of midwife, (4) Differing expectation of appointment and (5) Mode of assessment and time issues. DISCUSSION: Factors associated with high comfort and disclosure levels in this sample include an experienced and skilled midwifery workforce at the study site and a relatively advantaged and mental health literate sample. Proper implementation of psychosocial assessment policy; setting clear expectations for women and, for more vulnerable women, extending assessment time, modifying mode of assessment, and offering continuity of midwifery care will help build rapport, improve disclosure, and increase the chance of early identification and intervention. CONCLUSIONS: This study informs approaches to improving comprehensive psychosocial assessment in the maternity setting.
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".