Implementing cognitive behavioral therapy for psychosis: An international survey of clinicians’ attitudes and obstacles.
Bibliographic record
Abstract
OBJECTIVE: This study aimed to better understand the implementation of cognitive behavioral therapy for psychosis (CBTp) by exploring the impact of clinicians' attitudes toward CBTp within the Theory of Planned Behavior framework (i.e., by considering attitudes, behaviors, intention, and social norms) as well as perceived obstacles and response to proposed solutions. METHOD: One hundred forty-two clinicians from 2 sites in Canada and 1 site in Australia answered an online survey involving both Likert scales and open-ended questions. The role of attitudes, social norms, and behavioral control (i.e., freedom to decide or act) on intention of offering CBTp delivering CBTp were analyzed using linear and logistic regressions. Sites were compared using analysis of variance. Reponses to perceived obstacles were thematically analyzed. RESULTS: Results were similar across settings. Entered together in the model, attitudes, social norms, and behavioral control were significant in predicting the intention of offering CBTp, F(3, 125) = 38.49, p < .001, with 49% of the variance explained, although behavioral control did not significantly contribute to the model. CBTp training (odds ratio = 0.23, confidence interval = 0.06-0.58) and social norms (odds ratio = 0.79, confidence interval = 0.68-0.93) significantly predicted CBTp delivery. Six themes that emerged regarding perceived obstacles are provided. Training, supervision, and local support were the most frequently endorsed solutions. Brief or modular CBTp and group or online delivery were also positively endorsed. CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Clinicians' individual and collective attitudes should be targeted by more and better training to increase their delivery of CBTp. Given organizational barriers, CBTp-informed interventions warrant further investigation. (PsycINFO Database Record
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".