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Record W4361772745 · doi:10.2196/44722

The Impacts of a Psychoeducational Alcohol Resource During Internet-Delivered Cognitive Behavioral Therapy for Depression and Anxiety: Observational Study

2023· article· en· W4361772745 on OpenAlexafffundvenue
Vanessa Peynenburg, Ram P. Sapkota, Tristen Lozinski, Christopher Sundström, Andrew Wilhelms, Nickolai Titov, Blake F. Dear, Heather D. Hadjistavropoulos

Bibliographic record

VenueJMIR Mental Health · 2023
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of Regina
FundersMinistry of Health, SaskatchewanUniversity of Regina
KeywordsAnxietyObservational studyPsychoeducationAlcohol Use Disorders Identification TestDepression (economics)PsychiatryMedicineClinical psychologyPsychologyPoison controlInjury preventionPsychological interventionMedical emergency

Abstract

fetched live from OpenAlex

BACKGROUND: Problematic alcohol use is common among clients seeking transdiagnostic internet-delivered cognitive behavioral therapy (ICBT) for depression or anxiety but is not often addressed in these treatment programs. The benefits of offering clients a psychoeducational resource focused on alcohol use during ICBT for depression or anxiety are unknown. OBJECTIVE: This observational study aimed to elucidate the impacts of addressing comorbid alcohol use in ICBT for depression and anxiety. METHODS: All patients (N=1333) who started an 8-week transdiagnostic ICBT course for depression and anxiety received access to a resource containing information, worksheets, and strategies for reducing alcohol use, including psychoeducation, reasons for change, identifying risk situations, goal setting, replacing drinking with positive activities, and information on relapse prevention. We assessed clients' use and perceptions of the resource; client characteristics associated with reviewing the resource; and whether reviewing the resource was associated with decreases in clients' alcohol use, depression, and anxiety at posttreatment and 3-month follow-up among clients dichotomized into low-risk and hazardous drinking categories based on pretreatment Alcohol Use Disorders Identification Test (AUDIT) scores. RESULTS: During the 8-week course, 10.8% (144/1333) of clients reviewed the resource, and those who reviewed the resource provided positive feedback (eg, 127/144, 88.2% of resource reviewers found it worth their time). Furthermore, 18.15% (242/1333) of clients exhibited hazardous drinking, with 14.9% (36/242) of these clients reviewing the resources. Compared with nonreviewers, resource reviewers were typically older (P=.004) and separated, divorced, or widowed (P<.001). Reviewers also consumed more weekly drinks (P<.001), scored higher on the AUDIT (P<.001), and were more likely to exhibit hazardous drinking (P<.001). Regardless of their drinking level (ie, low risk vs hazardous), all clients showed a reduction in AUDIT-Consumption scores (P=.004), depression (P<.001), and anxiety (P<.001) over time; in contrast, there was no change in clients' drinks per week over time (P=.81). Reviewing alcohol resources did not predict changes in AUDIT-Consumption scores or drinks per week. CONCLUSIONS: Overall, ICBT appeared to be associated with a reduction in alcohol consumption scores, but this reduction was not greater among alcohol resource reviewers. Although there was some evidence that the resource was more likely to be used by clients with greater alcohol-related difficulties, the results suggest that further attention should be given to ensuring that those who could benefit from the resource review it to adequately assess the benefits of the resource.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.154
GPT teacher head0.510
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes3
Has abstractyes

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