Physician Communication in Injectable Opioid Agonist Treatment: Collecting Patient Ratings With the Communication Assessment Tool
Bibliographic record
Abstract
OBJECTIVE: Patient ratings of physician communication in the setting of daily injectable opioid agonist treatment are reported. Associations between communication items and demographic, health, drug use, and treatment characteristics are explored. METHODS: Participants (n = 121) were patients receiving treatment for opioid use disorder with hydromorphone (an opioid analgesic) or diacetylmorphine (medical grade heroin). Ratings of physician communication were collected using the 14-item Communication Assessment Tool. Items were dichotomized and associations were explored using univariate and multivariable logistic regression models for each of the 14 items. RESULTS: Ratings of physician communication were lower than reported in other populations. In nearly all of the 14 multivariable models, participants with more physical health problems and with lower scores for treatment drug liking had lower odds of rating physician communication as excellent. CONCLUSIONS: In physician interactions with patients with opioid use disorder, there is a critical need to address comorbid physical health problems and account for patient medication preferences. PRACTICE IMPLICATIONS: Findings reinforce the role physicians can play in communicating with patients about their comorbid conditions and about medication preferences. In the patient-physician interaction efforts to meet patients' evolving treatment needs and preferences can be made by offering patients access to all available evidence-based treatments.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".