Videoconferencing-Based Treatment of Alcohol Use Disorders: Analyses of Nonparticipation
Bibliographic record
Abstract
BACKGROUND: We recently conducted a small randomized controlled trial (RCT) aiming to examine the effectiveness of videoconferencing-based treatment of alcohol use disorders in a real-life setting. The patient and participation rates were lower than anticipated. OBJECTIVE: The objectives of our study were (1) to examine differences between participants and nonparticipants, and (2) to examine the characteristics of nonparticipants and their reported reasons for not participating. METHODS: First, we analyzed nonparticipation through a comparative analysis of participants and nonparticipants using data from a clinical database, covering all patients starting treatment at the clinic. Second, on the basis of data from an anonymous questionnaire filled out by nonparticipants, we analyzed barriers to participating and the descriptive sociodemographics of nonparticipants who reported technical barriers versus those who did not. RESULTS: Of 128 consecutive patients starting treatment during the study period, we found no significant differences between participants (n=71) and nonparticipants (n=51) according to sociodemographics, alcohol measures, and composite scores. Of 51 nonparticipants, 43 filled out the questionnaire with reasons for not participating. We derived 2 categories of barriers from the questionnaire: scientific barriers, which were barriers to the scientific study in general (n=6), and technical barriers, which were barriers to using a laptop or videoconferencing specifically (n=27). We found no significant differences in sociodemographics between nonparticipants who reported technical barriers to participating in the study and those who did not note technical barriers. A total of 13 patients elaborated on technical barriers, and 9 patients found videoconferencing impersonal, preferred personal contact, and would rather attend face-to-face treatment at the clinic. CONCLUSIONS: Patient barriers to participating in the RCT were mainly concerned with the technology. There were no significant differences between participants and nonparticipants, nor between nonparticipants who noted technical barriers to participating and those who did not. If a similar study is to be conducted or the solution is to be upscaled and implemented, attention should be given to the user friendliness of the technical equipment and the recruitment process, preparing the patients by emphasizing the information given to them about the technical equipment and its advantages.
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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 | 0.073 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".