Physicians’ attitudes towards office-based delivery of methadone maintenance therapy: results from a cross-sectional survey of Nova Scotia primary-care physicians
Bibliographic record
Abstract
BACKGROUND: Approximately 90,000 Canadians use opioids each year, many of whom experience health and social problems that affect the individual user, families, communities and the health care system. For those who wish to reduce or stop their opioid use, methadone maintenance therapy (MMT) is effective and supporting evidence is well-documented. However, access and availability to MMT is often inconsistent, with greater inequity outside of urban settings. Involving community based primary-care physicians in the delivery of MMT could serve to expand capacity and accessibility of MMT programs. Little is known, however, about the extent to which MMT, particularly office-based delivery, is acceptable to physicians. The aim of this study is to survey physicians about their attitudes towards MMT, particularly office-based delivery, and the perceived barriers and facilitators to MMT delivery. METHODS: In May 2008, facilitated by the College of Physicians and Surgeons of Nova Scotia, a cross-sectional, e-mail survey of 950 primary-care physicians practicing in Nova Scotia, Canada was administered via the OPINIO on-line survey software, to assess the acceptability of office-based MMT. Logistic regressions, adjusted for physician sociodemographic characteristics, were used to examine the association between physicians' willingness to participate in office-based MMT, and a series of measures capturing physician attitudes and knowledge about treatment approaches, opioid use, and methadone, as well as perceived barriers to MMT. RESULTS: Overall, 19.8% of primary-care physicians responded to the survey, with 56% who indicated that they would be willing to be involved in MMT under current or similar circumstances; however, willingness was associated with numerous attitudinal and systemic factors. The barriers to involvement in MMT that were frequently cited included a lack of training or experience in MMT, lack of support services, and potential challenges of working with an MMT patient population. CONCLUSIONS: Study findings provide valuable information to help facilitate greater involvement of primary-care physicians in MMT, while highlighting concerns around administration, support, and training. Even limited uptake by primary-care physicians would greatly enhance MMT access in Nova Scotia, particularly for methadone clients located in rural communities. These findings are applicable broadly, to any jurisdictions where office-based MMT is not currently available.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".