Managing Opioids, Including Misuse and Addiction, in Patients With Serious Illness in Ambulatory Palliative Care: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Pain and opioid management are core ambulatory palliative care skills. Existing literature on how to manage opioid misuse/use disorder excludes patients found in palliative care settings, such as individuals with serious illness or those at the end of life. OBJECTIVES: We conducted an exploratory study to: (1) Identify the challenges palliative care clinicians face when prescribing opioids in ambulatory settings and (2) explore factors that affect opioid decision-making. METHODS: We recruited palliative care clinicians who prescribe opioids in ambulatory settings, which included open-ended questions and was conducted online. Results were analyzed qualitatively using a content analysis-based approach. RESULTS: Eighty-three palliative care clinicians (mostly MDs/DOs) participated. Challenges faced when prescribing opioids included clinician differences in approach to care (eg, transitioning from another clinician with more permissive opioid prescribing), medication access (eg, inadequate pharmacy supply), resource constraints (eg, access to mental health and addiction expertise), managing problems outside the typical palliative care scope (eg addiction). Participants also discussed factors that influenced their opioid prescribing decisions, such as opioid-related harms and risks that they need to weigh; they also spoke about the necessity of considering other factors like the patient's environment, disease, treatment, and prognosis. CONCLUSION: This study highlights the challenge of opioid management in patients with serious illness, particularly when misuse or substance use disorder is present, and suggests areas for future research focus. Our next step will be to establish consensus on approaches to opioid prescribing decision-making and policy in seriously ill patients presenting to ambulatory palliative care.
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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 | 0.001 |
| 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".