Barriers to accessing substance use disorder treatment: a providers perspective
Bibliographic record
Abstract
Substance use disorders are gaining significant attention in the recent years and as such, questions have been raised of how we can help alleviate the problem of addictions. Many barriers exist that make receiving adequate treatment difficult. This results in long-term struggles with addictions, financial stresses, detriments on the health of individuals and unfortunately can have fatal outcomes. This study focuses on the barriers that healthcare providers in Winnipeg, Manitoba and surrounding areas face when referring patients for addictions treatment. A survey was sent out by email to multiple providers practicing in various areas of medicine that deal with addictions in one form or another. Participants were asked to rate in order of significance multiple barriers that they have faced. Respondents indicated that treatment wait times/capacity was the most significant barrier. Second most significant was difficulties for providers in determining patient eligibility for certain centres, followed by providers understanding of options available and lastly, issues with ongoing communication between provider and patient. A section of the survey also allowed for participants to leave comments on additional barriers they found to be relevant to their practice.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".