Investigating Alexithymia as a Moderator of Outcomes in a Randomized Controlled Trial of an Online Intervention for Co-Occurring Depression and Hazardous Alcohol Use: Enquête sur l'alexithymie en tant que modérateur des résultats dans un essai randomisé contrôlé d'une intervention en ligne pour la dépression concomitante et la consommation dangereuse d'alcool
Bibliographic record
Abstract
OBJECTIVE: Alexithymia is characterized by difficulty identifying and/or describing emotions, reduced imaginal processes, and externally oriented thinking. High levels of alexithymia may increase the challenge of supporting individuals with co-occurring depression and hazardous alcohol use. This secondary analysis sought to investigate whether or not alexithymia moderated the outcomes of an online intervention for depression and alcohol use. METHOD: As part of a randomized controlled trial, 988 participants were randomly assigned to receive an intervention dually focused on depression and alcohol use, or an intervention only focused on depression. The pre-specified mediation hypothesis was that changes in drinking at 3 months follow-up would effect the association between the intervention and change in depression at 6 months. This secondary analysis extends the investigation by adding alexithymia as a moderator. RESULTS: The current analysis demonstrated that including alexithymia as a moderator resulted in a conditional direct effect. Specifically, there was an intervention effect where participants who received the combined depression and alcohol intervention had larger improvements in their depression scores at 6 months, but this was only when their alexithymia score at baseline was also high (60.5 or higher). CONCLUSION: These results suggest that treatment planning and intervention effectiveness could be informed and optimized by taking alexithymia severity into consideration. This is especially merited as alexithymia can contribute to the weaker therapeutic alliance, more distress and dysphoria, shorter periods of abstinence, and more severe depression, compounding the complexity of supporting individuals with comorbid conditions. More research is needed to systematically investigate these possible modifying effects. PLAIN LANGUAGE TITLE: Does difficulty identifying/describing emotions or externally-oriented thinking influence the effectiveness of an intervention among people with both depression and hazardous alcohol use?
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".