Doctors’ views of disulfiram and their response to relapse in alcohol-dependent patients, Free State, 2009
Bibliographic record
Abstract
BACKGROUND: Disulfiram is the oldest and best known drug to prevent relapse after detoxification from alcohol. Effective use of the drug is dependent on stringent monitoring and high levels of external motivation. Doctors' perceptions about the drug have not been investigated extensively. AIM: We investigated the perceptions and practices of doctors involved in relapse prevention in alcoholics with regard to disulfiram and their response to relapse. SETTING: The study population consisted of 60 doctors from the Free State Province, involved in the follow-up of alcoholics across various work settings. METHODS: A cross-sectional descriptive study design was used, and data collection involved the use of a questionnaire and semi-structured interviews. Quantitative results are presented in figures and percentages to provide a background for the qualitative findings that are clustered in themes. RESULTS: A quarter of participants did not prescribe disulfiram, another quarter prescribed disulfiram routinely after detoxification, and half of them prescribed it for selected cases only. Subject to affordability, selection of disulfiram was mainly determined by the perceived level of the patient's motivation. External motivation sometimes took the form of threats of bodily harm or death caused by drinking. Some participants regarded relapse as confirmation of poor motivation and even a valid reason for terminating the doctor-patient relationship. CONCLUSION: Doctors perceive disulfiram as a psychological tool to induce motivation through creating fear of drinking. Failure and success are perceived as related to the level of motivation. These perceptions could be unfair as biological factors in inter-patient variability in response are ignored.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".