Codeine misuse and dependence in South Africa: Perspectives of addiction treatment providers
Bibliographic record
Abstract
BACKGROUND: General practitioners are referring patients with codeine-related problems to specialist treatment facilities, but little is known about the addiction treatment providers, the kinds of treatment they provide, and whether training or other interventions are needed to strengthen this sector. OBJECTIVES: To investigate the perspectives of addiction treatment providers regarding treatment for codeine misuse or dependence. METHOD: Twenty addiction treatment providers linked to the South African Community Epidemiology Network on Drug Use and the South African Addiction Medicine Society were contacted telephonically and asked 20 questions. RESULTS: While many participants had received training in pharmacological management of individuals with opioid dependence, only two had received specific training on codeine management. Between half and two-thirds of the treatment settings they worked in provided detoxification, pharmacotherapy, psychosocial treatment and aftercare. Very few treatment settings offered long-term treatment for codeine misuse and dependence. Participants indicated that over half of their codeine patients entered treatment for intentional misuse for intoxication, and dependence resulting from excessive or long-term use. The main barriers to patients entering treatment were seen as denial of having a problem, not being ready for change, mental health problems, stigma, and affordability of treatment. Participants identified a need for further training in how to manage withdrawal and detoxification, treatment modalities including motivational interviewing, and relapse prevention. CONCLUSIONS: Gaps in training among treatment providers need to centre on how to manage withdrawal from codeine use and detoxification, motivational interviewing and relapse prevention. Interventions are needed to address barriers to entering treatment, including user denial.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".