Gender differences in pathways to care for early psychosis
Bibliographic record
Abstract
AIMS: Gender is a critical demographic determinant in first-episode psychosis research. We used data from the ACE Pathways to Care Project, which examined pathways to care in African-origin, Caribbean-origin and European-origin participants, to investigate the role of gender in pathways to early intervention programmes. METHOD: A qualitative approach was used to examine gender differences in the routes to care. We conducted four focus groups and four individual in-depth interviews with 25 service users of early intervention services from African-origin, Caribbean-origin and European-origin populations. RESULTS: Gender stereotypes negatively influence the first service contact for women, and the early phase of the help seeking process for men. Women reported trying to seek care. However, family members and service providers often questioned their calls for help. Men described having difficulties in talking about their symptoms, as the act of seeking help was perceived as a sign of weakness by peers. CONCLUSIONS: The findings of this study suggest that gender stereotypes shape the journey to specialized care in different ways for men and women. Awareness of the impact that gender stereotypes have when a young person is seeking care for psychosis could help to promote a shift in attitudes among health-care providers and the provision of more compassionate and patient-centred care during this critical time.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".