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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".