Enhancing decision making in the selection of treatment for anxiety and related disorders
Bibliographic record
Abstract
Cognitive Behavioural Therapy (CBT) and pharmacotherapy are both effective treatments for anxiety and related disorders, with some evidence that CBT yields more durable gains and is more cost effective over the long term. Despite this, pharmacotherapy utilization rates have been on the rise in medical settings, while use of psychological treatments has declined. This bias suggests that patients may not be making informed choices about evidence based therapies, thereby increasing the suffering and economic burden associated with anxiety and related disorders. The purpose of this dissertation was to examine variables related to treatment choice in anxiety and related disorders, and to explore methods for improving the selection process to promote informed choice of therapy. The first phase of the project focused on considerations that may be unique to individuals selecting treatment for anxiety and related disorders, to examine variables that may be associated with past and present treatment choices. The second phase drew upon research on decision making and treatment selection from social psychology to formulate hypotheses about how treatment selection for anxiety and related disorders can be improved. Adults with significant anxiety symptoms (n= 105) were randomly assigned to one of three conditions in which they were presented with arguments in favour of psychological treatment and pharmacological treatment. The conditions varied on relative strength of argument combinations for each modality. In keeping with the Elaboration Likelihood Model (Petty & Cacioppo, 1981, 1986a), it was predicted that strong arguments would only be persuasive if participants had the ability and motivation to scrutinize them. The majority of participants rated arguments for psychological treatment as stronger, regardless of condition. Participants displayed a strong preference for psychological treatment, across forced choice measures and rating scales, and tended to endorse psychological attributions for their anxiety. Findings from this study could inform efforts aimed at promoting critical and informed decision making in treatment for anxiety and related disorders. In addition, findings could be of great importance to dissemination efforts aimed at meeting patient preferences for anxiety treatment and increasing the uptake of CBT.
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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 | 0.038 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".