Disclosure of sensitive material at routine antenatal psychosocial assessment: The role of psychosocial risk and mode of assessment
Bibliographic record
Abstract
PROBLEM: While routine psychosocial assessment is acceptable to most pregnant women, some women will not fully disclose psychosocial concerns to their clinician. AIMS: To assess the impact of psychosocial risk, current symptoms and mode of assessment on women's honesty of disclosure at psychosocial assessment. METHODS: Logistic regression was used to examine associations between disclosure and a range of psychosocial characteristics in women who were 'always honest' and 'not always honest'. Mixed ANOVAs were used to test the influence of mode of assessment and honesty on scores on a repeated measure of psychosocial risk. FINDINGS: 10.8% (N=193 of 1788) of women did not fully disclose at psychosocial assessment. Non-disclosure was associated with a mental health history (aOR=1.78, 95%CI: 1.18-2.67, p<0.01) and lack of social and partner support (aOR=1.74, 95%CI: 1.16-2.62, p<0.05; aOR=2.08, 95%CI: 1.11-3.90, p<0.05, respectively). Those reporting not always being honest at face to face assessment showed a greater increase in psychosocial risk score when the assessment was repeated online via self-report, compared to women who were always honest. DISCUSSION: A history of mental health issues and lack of social and partner support are associated with reduced disclosure at face to face assessment. Online self-report assessment may promote greater disclosure, however this should always be conducted in the context of clinician feedback. CONCLUSION: Greater psychosocial vulnerability is associated with a lower likelihood of full disclosure. Preliminary findings relating to mode of assessment warrant further exploration within a clinical context.
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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 | 0.101 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".