Decision: Interventions for improving adherence to psychological treatments for common mental disorders: a systematic review — R0/PR3
Bibliographic record
Abstract
Our systematic review aims to synthesise the evidence on interventions targeting improvement in patient adherence to psychological treatments for common mental disorders. A search was conducted on six electronic databases using search terms under the following concepts: common mental disorders, adherence, psychological treatments and controlled trial study design. Due to the heterogeneity in intervention content and outcomes evaluated in the included studies, a narrative synthesis was conducted. Risk of bias was assessed using the Cochrane Risk of Bias Version 2 tool for randomised controlled trials and the Cochrane ROBINS-I tool for non-randomised controlled trials. The search yielded 23 distinct studies with a total sample size of 2,779 participants. All studies were conducted in high-income or upper-middle-income countries. Interventions to improve patient adherence to psychological treatments included reminders and between-session engagement (e.g., text messages), motivational interviewing, therapy orientation (e.g., expectation-setting) and overcoming structural barriers (e.g., case management). Interventions from 18 out of 23 studies were successful in improving at least one primary adherence outcome of interest (e.g., session attendance). Some studies also reported an improvement in secondary outcomes – six studies reported an improvement in at least one clinical outcome (e.g., depression), and three studies reported improvements in at least one measure of well-being or disability (e.g., days spent in in-patient treatment). By incorporating these interventions into psychological treatment services, therapists can better engage with and support their patients, potentially leading to improved mental health outcomes and overall well-being.
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 | 0.037 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".