Clinician Perspectives on Opioid Treatment Agreements: A Qualitative Analysis of Focus Groups
Bibliographic record
Abstract
BACKGROUND: Patients with chronic pain face significant barriers in finding clinicians to manage long-term opioid therapy (LTOT). For patients on LTOT, it is increasingly common to have them sign opioid treatment agreements (OTAs). OTAs enumerate the risks of opioids, as informed consent documents would, but also the requirements that patients must meet to receive LTOT. While there has been an ongoing scholarly discussion about the practical and ethical implications of OTA use in the abstract, little is known about how clinicians use them and if OTAs themselves modify clinician prescribing practices. OBJECTIVE: To determine how clinicians use OTAs and the potential impacts of OTAs on opioid prescribing. DESIGN: We conducted qualitative analysis of four focus groups of clinicians from a large Midwestern academic medical center. Groups were organized according to self-identified prescribing patterns: two groups for clinicians who identified as prescribers of LTOT, and two who did not. PARTICIPANTS: 17 clinicians from General Internal Medicine, Family Medicine, and Palliative Care were recruited using purposive, convenience sampling. APPROACH: Discussions were recorded, transcribed, and analyzed for themes using reflexive thematic analysis by a multidisciplinary team. KEY RESULTS: Our analysis identified three main themes: (1) OTAs did not influence clinicians' decisions whether to use LTOT generally but did shape clinical decision-making for individual patients; (2) clinicians feel OTAs intensify the power they have over patients, though this was not uniformly judged as harmful; (3) there is a potential misalignment between the intended purposes of OTAs and their implementation. CONCLUSION: This study reveals a complicated relationship between OTAs and access to pain management. While OTAs seem not to impact the clinicians' decisions about whether to use LTOT generally, they do sometimes influence prescribing decisions for individual patients. Clinicians shared complex views about OTAs' purposes, which shows the need for more clarity about how OTAs could be used to promote shared decision-making, joint accountability, informed consent, and patient education.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".