The Substance Abuse Treatment Workforce of South Africa
Bibliographic record
Abstract
The purpose of this paper is to describe characteristics of substance abuse treatment counselors in the Republic of South Africa, including demographics, education, training, and job duties. Counselors recruited from 24 treatment centers completed a survey after signing informed consent. Counselors were primarily female (75%), racially diverse (36.4% White, 30.8% Black, 18.9% Coloured, 12.6% Indian or Asian, and 1.4% Cape Malay), and were 38 years old on average. The majority (62.3%) held at least an equivalent of a bachelor's degree, and just under half (49%) were registered social workers. Counselors had a mean of 5.3 years' experience in substance abuse treatment. The substance abuse treatment workforce of South Africa appears to be young and educated, yet only one third of the counselors had any formal training in Cognitive Behavioral Therapy. South African counselors could benefit from more training in evidence-based techniques.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".