Reframing conceptualizations of primary care involvement in opioid use disorder treatment
Bibliographic record
Abstract
Abstract Background Opioid-related harms and opioid use disorder (OUD) are health priorities requiring urgent policy responses. There have been many calls for improved OUD care in primary care, as well as increasing involvement of primary care providers in countries like Canada and Australia, which have been experiencing high rates of opioid-related harms. Methods Using Starfield’s 4Cs conceptualization of primary care functions, we examined how and why primary care systems may be suited towards, or pose challenges to providing OUD care, and identified health system opportunities to address these challenges. We conducted 14 semi-structured interviews with 16 key informants with experience in opioid use policy in Canada and Australia. Results Primary care was identified to be an ideal setting for OUD care delivery due to its potential as the first point of contact in the health system; the opportunity to offer other health services to people with OUD; and the ability to coordinate care with other health providers (e.g. specialists, social workers) and thus also provide care continuity. However, challenges include a lack of resources and support for chronic disease management more broadly in primary care, and the prevailing model of OUD treatment, where addictions care is not seen as part of comprehensive primary care. Additionally, the highly regulated OUD policy landscape is also a barrier, manifesting as a ‘regulatory cascade’ in which restrictive oversight of OUD treatment passes from regulators to health providers to patients, normalizing the overly restrictive nature and inaccessibility of OUD care. Conclusions While primary care is an essential arena for providing OUD care, existing sociocultural, political, health professional, and health system factors have led to the current model of care that limits primary care involvement. Addressing this may involve structurally embedding OUD care into primary care and strengthening primary care in general.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.058 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".