Adherence to self-care interventions for depression or anxiety: A systematic review
Bibliographic record
Abstract
Objective: The objective of this study was to synthesise and describe adherence to intervention in published studies of supported self-care for depression or anxiety, and to identify participant characteristics associated with higher adherence. Methods: We searched the databases EMBASE, MEDLINE, CINAHL, and PSYCINFO for the period from January 1986 until September 2010. Eligible studies reporting on adherence to supported self-care interventions for depression or anxiety symptoms were identified. Results: We identified 40 studies of supported self-care interventions for depression and anxiety, of which 22 (55%) reported any measure of adherence to the intervention. Among these 22 studies, 18 (82%) reported the percentage of participants completing the entire self-care tool (20%–93%; Mean = 66%, SD 17), 13 studies reported the amount of self-care tools completed by the average participant (50.6%–96.4%; Mean = 80%, SD 11.6). Four studies (18%) reported the frequency of contacts with the self-care guide. Three (14%) studies reported participant characteristics associated with adherence. Conclusion: Overall, reported adherence levels to supported self-care interventions for depression and anxiety indicate a significant amount of patient involvement in these interventions. Routine reporting of adherence will improve our understanding of adherence to supported self-care interventions, and will allow researchers to link adherence with intervention outcome.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".