Client Contact in Self-Help Therapy for Anxiety and Depression: Necessary But Can Take a Variety of Forms Beside Therapist Contact
Bibliographic record
Abstract
Self-administered therapies (SATs) have been promoted as a way to increase access to evidence-based mental health services. Recent meta-analyses and literature reviews suggest that SATs with clinical guidance are more effective than SATs with no contact for the treatment of anxiety and depression. However, little attention has been paid to the role of nonguidance contact, contact that does not involve the provision of assistance in the application of specific therapy techniques such as emails to encourage treatment adherence. The present article examines the impact of nonguidance contact on the outcomes of SATs for anxiety and depression. Electronic databases were searched to identify studies conducted over the past two decades by independent research teams that have tested cognitive-behavioural SATs over multiple trials. Findings suggest that the involvement or guidance of a therapist is not essential for SATs to produce significant benefits as long as nonguidance contact is provided. It is suggested that even very minimal levels of nonguidance contact increase SAT's outcomes by motivating treatment engagement and improving adherence. The benefit of SATs that can be accessed directly by large numbers of individuals and that do not require therapist involvement to ensure efficacy can potentially significantly increase the cost effectiveness and quality of mental health service delivery.
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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".