Estimating the effectiveness of an enhanced ‘Improving Access to Psychological Therapies’ (IAPT) service addressing the wider determinants of mental health: a real-world evaluation
Bibliographic record
Abstract
BACKGROUND: Addressing the wider determinants of mental health alongside psychological therapy could improve mental health service outcomes and population mental health. OBJECTIVES: To estimate the effectiveness of an enhanced 'Improving Access to Psychological Therapies' (IAPT) mental health service compared with traditional IAPT in England. Alongside traditional therapy treatment, the enhanced service included well-being support and community service links. DESIGN: A real-world evaluation using IAPT's electronic health records. SETTING: Three National Health Service IAPT services in England. PARTICIPANTS: Data from 17 642 service users classified as having a case of depression and/or anxiety at baseline. INTERVENTION: We compared the enhanced IAPT service (intervention) to an IAPT service in a different region providing traditional treatment only (geographical control), and the IAPT service with traditional treatment before additional support was introduced (historical control). PRIMARY OUTCOME MEASURES: Patient Health Questionnaire-9 (PHQ-9) Depression Scale (score range: 0-27) and Generalised Anxiety Disorder-7 (GAD-7) Anxiety Scale (score range: 0-21); for both, lower scores indicate better mental health. Propensity scores were used to estimate inverse probability of treatment weights, subsequently used in mixed effects regression models. RESULTS: Small improvements (mean, 95% CI) were observed for PHQ-9 (depression) (-0.21 to -0.32 to -0.09) and GAD-7 (anxiety) (-0.23 to -0.34 to -0.13) scores in the intervention group compared with the historical control. There was little evidence of statistically significant differences between intervention control and geographical control. CONCLUSIONS: Embedding additional health and well-being (H&W) support into standard IAPT services may lead to improved mental health outcomes. However, the lack of improved outcomes compared with the geographical control may instead reflect a more general improvement to the intervention IAPT service. It is not clear from our findings whether an IAPT service with additional H&W support is clinically superior to traditional IAPT models.
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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 | 0.049 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".