Primary care management of alcohol use disorder in rural, remote, and northern settings
Bibliographic record
Abstract
Alcohol consumption is a leading cause of substance-related morbidity and mortality globally.In Canada, there are established Guidelines for Low-Risk Drinking, which are designed to reduce the risk of disease, injury, or death by outlining recommended maximum volumes of, and frequency for, alcohol consumption (Butt, Beirness, Gliksman, Paradis, & Stockwell, 2011).Exceeding the recommended limits places individuals at risk of developing alcohol use disorder (AUD), and subsequently increases the likelihood of alcohol-related adverse health outcomes.In rural, remote, and northern British Columbia (BC), there are significant rates of AUD and alcohol-related morbidity and mortality.In these geographic areas, the responsibility for recognition and treatment of patients with AUD usually resides with the primary care provider.Primary care management of patients with AUD in BC is supported by evidence-based treatment guidelines; however, these guidelines suggest that certain patients may benefit from referral to specialist AUD services, which may be a barrier to treatment in this geographic context.In rural, remote, and northern BC, primary care providers often experience significant barriers to referral of patients outside of the home community, suggesting that the guidelines may be discordant with the realities of AUD treatment in these areas.In order to improve AUD treatment, participation and success within patients' home communities, an integrative review was conducted to assess the optimum primary care treatment modalities within rural, remote, and northern settings.The findings from this integrative review suggest that there are some modifications to current primary care practice, which could benefit patients with AUD in rural, remote, and northern BC.In order to enhance treatment options for future patients with AUD, recommendations for primary care practice, nurse practitioner education, and further research are proposed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".