Predictors of treatment attrition of cognitive health interventions in first episode psychosis
Bibliographic record
Abstract
AIM: Dropping out of psychological interventions is estimated to occur in up to a third of individuals with psychosis. Given the high degree of attrition in this population, identifying predictors of attrition is important to develop strategies to retain individuals in treatment. We observed a particularly high degree of attrition (48%) in a recent randomized controlled study assessing cognitive health interventions for first-episode psychosis participants with comorbid social anxiety. Due to the importance of developing interventions for social anxiety in first episode psychosis, the aim of the present study was to identify putative predictors of attrition through a secondary analysis of data. METHODS: Participants (n = 96) with first episode psychosis and comorbid social anxiety were randomized to receive cognitive behavioural therapy for social anxiety or cognitive remediation. Differences between completers and non-completers (<50% intervention completed) were compared using t-tests or chi-square analyses; statistically significant variables were entered into a multivariate logistic regression model. RESULTS: Non-completers tended to be younger, had fewer years of education and had lower levels of social anxiety compared to completers. Lower baseline social anxiety and younger age were statistically significant predictors of non-completion in the logistic regression model. CONCLUSIONS: Age and social anxiety were predictors of attrition in cognitive health interventions in first episode psychosis populations with comorbid social anxiety. In the ongoing development of social anxiety interventions for this population, future studies should investigate specific engagement strategies, intervention formats and outcome monitoring to improve participant retention in treatment.
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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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".