Accepting new patients who require opioids into family practice: results from the MAAP-NS census survey study
Bibliographic record
Abstract
BACKGROUND: Acceptance to a family practice is key to access and continuity of care. While Canadian patients increasingly report not being able to acquire acceptance to a family practice, little is known about the association between requiring opioids and acceptance. We aim to determine the proportion of family physicians who would accept new patients who require opioids and describe physician and practice characteristics associated with willingness to accept these patients. METHODS: Census telephone survey of family physicians' practices in Nova Scotia, Canada. MEASURES: physician (i.e., age, sex, years in practice) and practice (i.e., number/type of provider in the practice, care hours/week) characteristics and practice-reported willingness to accept new patients who require opioids. RESULTS: The survey was completed for 587 family physicians (83.7% response rate). 354 (60.3%) were taking new patients unconditionally or with conditions; 326 provided a response to whether they would accept new patients who require opioids; 91 (27.9%) reported they would not accept a new patient who requires opioids. Compared to family physicians who would not accept patients who require opioids, in bivariate analysis, those who would, tended to work in larger practices; had fewer years in practice; are female; and provided more patient care. The relationship to number of providers in the practice, having a nurse, and experience persisted in multivariate analysis. CONCLUSIONS: The strongest predictors of willingness to accept patients who require opioids are fewer years in practice (OR = 0.96 [95% CI 0.93, 0.99]) and variables indicating a family physician has support of a larger (OR = 1.19 [95% CI 1.00, 1.42]), interdisciplinary team (e.g., nurses, mental health professionals) (OR = 1.15 [95% CI 1.11, 5.05]). Almost three-quarters (72.1%) of surveyed family physicians would accept patients requiring opioids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".