“It beats the hell out of going to a hospital”: service user experiences of telemedicine-based symptom-triggered alcohol withdrawal management
Bibliographic record
Abstract
INTRODUCTION: Increasingly, services for the management of substance use disorders have been developed or adapted for remote delivery. Limited research has investigated service user experience of these services. We undertook a qualitative sub-study, embedded within a pilot feasibility study of remote symptom-triggered alcohol withdrawal management, to better understand the experiences of participants. Our aim was to determine the acceptability of the intervention and refine intervention procedures. METHODS: Eligible participants were enrolled in the parent study and completed at least one day of telemedicine-delivered symptom-triggered alcohol withdrawal management. Individuals were adults with alcohol use disorder recruited using intensity sampling. Participants completed an audio-recorded, semi-structured interview. Thematic analysis was conducted using Braun and Clarke interpretive methodology. RESULTS: Fourteen individuals were enrolled in the study. Six themes were identified: benefits of being in the home environment, technological tensions, intervention-specific feedback, personal motivations for participation, post-program achievements and changes and navigating the 'system'. Participants identified numerous benefits of being in the home environment including: increased comfort, privacy and security, normalizing abstinence in the home, flexibility to engage in other tasks, and the convenience of not travelling. Intervention-specific feedback included positive aspects of the intervention (interactions with staff, accountability, counselling, use of medication), areas for improvement (preparation, scheduling, medication logistics, and aftercare), and the meaning and role of having a support person available during treatment. CONCLUSION: Participants found remote alcohol withdrawal management to be satisfactory and associated with several benefits including increased comfort, privacy, normalizing abstinence in the home, flexibility and convenience. They also provided important feedback for refinement of the intervention. Findings suggest that remote alcohol withdrawal management could play an important role in improving access to medical management of alcohol withdrawal, particularly in rural, remote or underserved areas.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".