Demand management processes to improve access to cognitive-behavioral therapies for anxiety disorders: a grounded theory study
Bibliographic record
Abstract
Introduction: Anxiety disorders are impactful mental health conditions for which evidence-based treatments are available, notably cognitive-behavioral therapies (CBTs). Even when CBTs are available, demand-side factors limit their access, and actors in a position to perform demand management activities lack a framework to identify context-appropriate actions. Methods: We conducted a constructivist grounded theory study in Quebec, Canada, to model demand management targets to improve access to CBTs for anxiety disorders. We recruited key informants with diverse experiences using purposeful, then theoretical sampling. We analyzed data from 18 semi-directed interviews and 20 documents through an iterative coding process centered around constant comparison. Results: The resulting model illustrates how actors can target clinical-administrative processes fulfilling the demand management functions of detection, evaluation, preparation, and referral to help patients progress on the path of access to CBTs. Discussion: Modeling clinical-administrative processes is a promising approach to facilitate leveraging the competency of actors involved in demand management at the local level to benefit public mental health.
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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 | 0.029 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".