The impact of shared decision-making on the treatment of anxiety and depressive disorders: systematic review
Bibliographic record
Abstract
Background Shared decision-making encourages patients to explore treatment options/choices in collaboration with their healthcare provider, inclusive of the best available evidence and the patient's values/preferences. Several effective treatments exist for people with anxiety and/or depressive disorders; shared decision-making may be particularly useful in this context. Aims To investigate whether shared decision-making enhances clinical outcomes in adults with anxiety and/or depressive disorders. Method A systematic review was conducted. Five electronic health databases were searched from database inception until August 2019, in addition to reference lists of included studies. Prospective controlled studies of shared decision-making in adults (aged 18–64 years) diagnosed with an anxiety and/or depressive disorder were included. Two reviewers independently conducted each stage of the review process. Results Six randomised controlled trials (N = 1834 participants) were included. Patient satisfaction improved in four studies. Patients were more likely to receive adequate treatment for depression in three studies. Anxiety symptoms decreased in one study. Patient involvement in decision-making increased in three studies. Because of the lack of blinded interventions and outcome assessment, the included studies were at moderate risk of bias. The certainty of evidence ranged from low to moderate, per GRADE criteria. Conclusions Shared decision-making shows promise for enhancing quality-of-care outcomes such as patient satisfaction, without increasing consultation time, but appears unlikely to improve symptoms of depression. However, it appears to be understudied in patients with anxiety disorders. Heterogeneity regarding definition and measurement of shared decision-making posed challenges for interpreting the results. More research is recommended to advance the field.
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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 | 0.041 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".