What Are The Help-Seeking Attitudes, Intentions and Behaviours of Female University Students of Colour Living in the UK?
Bibliographic record
Abstract
For decades, surveys have shown that women of colour (WOC) in the UK experience disproportionate rates of mental health problems compared to their white counterparts (Bailey & Tribe, 2021; Jongsma et al., 2019). Despite this, WOC have some of the lowest rates of engagement with mental health services (Dixon et al., 2016). As intersectionality is still an emerging framework for understanding mental health (Taylor & Richards, 2019; Lal et al., 2021), the body of literature on help-seeking behaviours in WOC is lacking, especially compared to the number of studies conducted on white participants. The proposed study explores the attitudes of this population towards mental health and help-seeking, understanding how these are influenced by their multiple intersecting identities of gender identity, ethnic background and diaspora membership Bailey, N. and Tribe, R. (2021) ‘A qualitative study to explore the help-seeking views relating to depression among older Black Caribbean adults living in the UK’, International Review of Psychiatry, 33(1-2), pp. 113-118. Jongsma, H. et al. (2019) ‘International incidence of psychotic disorders, 2002-27: a systematic review and meta-analysis’, The Lancet Public Health, 4(5) pp. 229-244. Dixon, L. et al. (2016) ‘Treatment engagement of individuals experiencing mental illness: review and update’, World Psychiatry, 15(1), pp.13-20. Taylor, D. and Richards, D. (2019) ‘Triple Jeopardy: Complexities of Racism, Sexism, and Ageism on the Experiences of Mental Health Stigma Among Young Canadian Black Women of Caribbean Descent’, Frontiers in Sociology, 4. Lal, R. et al. (2021) ‘Mental health seeking behaviour of women university students: An intersectional analysis’, International Health Trends and Perspectives, 1(2), pp. 288-307.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".