Strategies to improve access to cognitive behavioral therapies for anxiety disorders: A scoping review
Bibliographic record
Abstract
BACKGROUND: Strategies to improve access to evidence-based psychological treatments (EBPTs) include but are not limited to implementation strategies. No currently available framework accounts for the full scope of strategies available to allow stakeholders to improve access to EBPTs. Anxiety disorders are common and impactful mental conditions for which EBPTs, especially cognitive-behavioral therapies (CBT), are well-established yet often hard to access. OBJECTIVE: Describe and classify the various strategies reported to improve access to CBT for anxiety disorders. METHODS: Scoping review with a keyword search of several databases + additional grey literature documents reporting on strategies to improve access to CBT for anxiety disorders. A thematic and inductive analysis of data based on grounded theory principles was conducted using NVivo. RESULTS: We propose to classify strategies to improve access to CBT for anxiety disorders as either "Contributing to the evidence base," "Identifying CBT delivery modalities to adopt in practice," "Building capacity for CBT delivery," "Attuning the process of access to local needs," "Engaging potential service users," or "Improving programs and policies." Each of these strategies is defined, and critical information for their operationalization is provided, including the actors that could be involved in their implementation. IMPLICATIONS: This scoping review highlights gaps in implementation research regarding improving access to EBPTs that should be accounted for in future studies.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".