Healthcare avoidance by people who inject drugs in Bangkok, Thailand
Bibliographic record
Abstract
BACKGROUND: Although people who inject drugs (IDU) often contend with various health-related harms, timely access to health care among this population remains low. We sought to identify specific individual, social and structural factors constraining healthcare access among IDU in Bangkok, Thailand. METHODS: Data were derived from a community-recruited sample of IDU participating in the Mitsampan Community Research Project between July and October 2011. We assessed the prevalence and correlates of healthcare avoidance due to one's drug use using multivariate logistic regression. RESULTS: Among 437 participants, 112 (25.6%) reported avoiding health care because they were IDU. In multivariate analyses, factors independently associated with avoiding health care included having ever been drug tested by police [adjusted odds ratio (AOR) = 1.80], experienced verbal abuse (AOR = 3.15), been discouraged from engaging in usual family activities (AOR = 3.27), been refused medical care (AOR = 10.90), experienced any barriers to health care (AOR = 4.87) and received healthcare information and support at a drop-in centre (AOR = 1.92) (all P < 0.05). CONCLUSIONS: These findings highlight the need to address the broader policy environment, which perpetuates the criminalization and stigmatization of IDU, and to expand peer-based interventions to facilitate access to health care for IDU in this setting.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".