Prescription Medication Use among an Aboriginal Population Accessing Addiction Treatment
Bibliographic record
Abstract
OBJECTIVES: Inappropriate prescription medication use can have significant consequences. Although it is suspected that Aboriginal populations within Canada have high rates of inappropriate use, published information is lacking. To better understand this issue, we studied an Aboriginal population seeking addiction treatment. METHODS: We surveyed Aboriginal clients who accessed addiction treatment in Calgary, Alberta, for prescription medication use in the previous year, frequency of medication use, and medication source(s), if inappropriately used. RESULTS: Sixty-nine percent of the clients completed the survey (n = 144). Most respondents were aged 31 to 50 years (56%), and 52% were male. Of the respondents, 48% reported that they used prescription medication inappropriately, 8% indicated appropriate use, and the rest indicated no medication use. Sedatives or relaxants were most frequently used inappropriately. Among those who inappropriately used medication, 47% used medication more than 10 times in the previous year. Common sources for those who used medication inappropriately included medication given by a friend or a stranger (52%), medication bought on the street (45%), and medication prescribed by a physician (41%). Age greater than or equal to 30 years was associated with inappropriate use. Sex, residence, and Aboriginal status were not found to be associated with inappropriate use. CONCLUSION: Inappropriate prescription medication use was a significant problem among an Aboriginal population that sought addiction treatment, and many of these individuals accessed medication from a prescribing physician.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".