Has increased provision of treatment reduced the prevalence of common mental disorders? Review of the evidence from four countries
Bibliographic record
Abstract
Many people identified as having common mental disorders in community surveys do not receive treatment. Modelling has suggested that closing this "treatment gap" should reduce the population prevalence of those disorders. To evaluate the effects of reducing the treatment gap in industrialized countries, data from 1990 to 2015 were reviewed from four English-speaking countries: Australia, Canada, England and the US. These data show that the prevalence of mood and anxiety disorders and symptoms has not decreased, despite substantial increases in the provision of treatment, particularly antidepressants. Several hypotheses for this lack of improvement were considered. There was no support for the hypothesis that reductions in prevalence due to treatment have been masked by increases in risk factors. However, there was little evidence relevant to the hypothesis that improvements have been masked by increased reporting of symptoms because of greater public awareness of common mental disorders or willingness to disclose. A more strongly supported hypothesis for the lack of improvement is that much of the treatment provided does not meet the minimal standards of clinical practice guidelines and is not targeted optimally to those in greatest need. Lack of attention to prevention of common mental disorders may also be a factor. Reducing the prevalence of common mental disorders remains an unsolved challenge for health systems globally, which may require greater attention to the "quality gap" and "prevention gap". There is also a need for nations to monitor outcomes by using standardized measures of service provision and mental disorders over 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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".