Differences in professional help-seeking for mental health problems among migrants and non-migrants: Symptom severity, self-perceived mental health problem, and region of origin matter
Bibliographic record
Abstract
AIMS: To examine differences in help-seeking for mental health problems among migrants and non-migrants in Norway and to consider the role of symptom severity, acknowledgement of a mental health problem and region of origin. METHODS: We used data from a cross-sectional, online Norwegian Country Public Health Survey conducted in 2021. A total of 32,126 people, aged 18+ years, were included in the analyses, of which 8% were migrants. Around 60% of these were from countries within the European Economic Area, associated countries or the UK, USA, Canada, Australia and New Zealand (EEA+). RESULTS: Migrants from non-EEA+ countries showed higher odds of having sought professional mental health help than non-migrants, but this difference attenuated when controlling for sociodemographic factors, psychological distress and self-reported depression. An interaction revealed that at higher, but not lower, levels of psychological distress, non-EEA+ migrants had significantly lower odds of having sought help. Moreover, a stratified analysis indicated that this applied only to those without self-reported depression. CONCLUSIONS: At high levels of psychological distress, people from non-EEA+ regions living in Norway may not be getting professional support for mental health problems to the same extent as EEA+ migrants and non-migrants. This may especially be the case for those who do not perceive their symptoms as a mental health problem. Consequently, improving the ability to recognise mental health problems may be a possible avenue for reducing the treatment gap for migrants with high symptom levels, though longitudinal studies would be required to confirm this.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".