A prospective longitudinal analysis of opioid and stimulant use in American Indian and First Nations communities
Bibliographic record
Abstract
We describe stages of opioid and psychostimulant use (i.e., onset of use and progression to dependence) over the early life course within Indigenous communities where drug overdose impacts have been most extreme. This community-based participatory research includes 9 waves of survey data collected prior to and during the unfolding of the overdose epidemic in North America (2002-2017) on/near 8 distinct American Indian reservations or First Nation reserves. Substance use (psychostimulants, prescription pain pill misuse, and heroin) was assessed via structured diagnostic interviews. Discrete time survival analysis was used to estimate hazard rates for substance use initiation, dependence, and transitions from first use to dependence. Cumulative probability of lifetime use by age 27 years was 29 % for psychostimulants (e.g., methamphetamine), 35 % for prescription pain pills, and 22 % for heroin. New cases of prescription pain pill misuse peaked in the early 2000s, followed by rapid increases in psychostimulant and heroin use starting around 2008. We found that 37 %-51 % of drug users eventually met criteria for dependence, and progression from use to dependence was typically within one year for this sub-group. Socio-demographic factors were differentially associated with probability of substance use, depending on drug type. Findings highlight the critical need for culturally grounded prevention, harm reduction, and treatment strategies to address substance use inequities in Indigenous communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".