Treatment challenges for alcohol service users in Kampala, Uganda
Bibliographic record
Abstract
Kalema, D., Vanderplasschen, W., Vindevogel, S., Baguma, P., & Derluyn, I. (2017). Treatment challenges for alcohol service users in Kampala, Uganda. The International Journal Of Alcohol And Drug Research, 6(1), 27-35. doi:http://dx.doi.org/10.7895/ijadr.v6i1.240Background and Aims: Enhancing treatment participation of persons with substance use disorders is a challenge worldwide. Obstacles keeping people from entering or continuing treatment are well documented in Western countries, but such knowledge is scarce in majority countries that face particular challenges when implementing alcohol policies. This study aimed at identifying factors challenging treatment participation in Uganda, a Sub-Saharan country with a considerable alcohol problem.Methods: Data were collected during 30 in-depth, qualitative interviews on treatment challenges with 20 service providers and 10 male service users, who were recruited at one public and one private alcohol treatment center in the Ugandan capital city, Kampala. Men comprise about 90% of the total number of service users in these centers. Interview data were analyzed thematically, using Nvivo software, and were categorized around three levels of treatment challenges: societal, institutional, and personal challenges.Findings: Interview findings showed several treatment challenges relating to institutional aspects like inadequate human resources, overall insufficiency of services, and the treatment philosophy of available services. Respondents identified stigma and cultural interference as important challenges at the societal level, while limited awareness about addiction and denial of problems can be situated at the individual level.Conclusions: Institutional, societal, and personal challenges keep persons with AUD from participating in alcohol treatment in public and private services in Uganda. Alcohol regulation, sensitization, and prevention are needed to raise awareness at the societal and individual level, while appropriate training and additional financial resources may help to overcome institutional challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".