Is the self-reporting of mental health problems sensitive to public stigma towards mental illness? A comparison of time trends across English regions (2009–19)
Bibliographic record
Abstract
PURPOSE: The prevalence of mental health problems has rapidly increased over time. The extent to which this captures changes in self-reporting due to decreasing stigma is unclear. We explore this by comparing time trends in mental health and stigma-related indicators across English regions. METHODS: We produced annual estimates of self-reported mental disorders (SRMDs) across waves of the Health Survey for England (2009-18, n = 78,226) and three stigma-related indicators (knowledge, attitudes, and intended behaviour) across waves of the Attitudes Towards Mental Illness survey (2009-19, n = 17,287). Differences in trends were tested across nine Government Office Regions using linear models, adjusting for age, sex, ethnicity, marital status, and social class. RESULTS: In 2009, SRMDs did not vary by region (p = 0.916), whereas stigma-related indicators did (p < 0.001), with London having the highest level of stigma and the North East having lowest level of stigma. Between 2009 and 2018, the prevalence of SRMDs increased from 4.3 to 9.1%. SRMDs increased and stigma-related indicators improved at different rates across regions over time (SRMDs p = 0.024; stigma-related indicators p < 0.001). London reported the lowest increase in SRMDs (+ 0.3 percentage point per year) yet among the largest improvements in attitudes and intended behaviour across regions. CONCLUSIONS: Improvements in attitudes towards mental illness did not mirror changes in self-reported mental health problems across English regions over the past decade. The findings do not support the argument that changes in public stigma, at least when defined at this regional scale, have been driving the increase in self-reported mental health indicators in recent years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".