Safe Prescribing Practices: Clinicians' Views on Prescribing Opioids to Patients With Early-Stage Cancer
Bibliographic record
Abstract
PURPOSE: Opioids are often necessary for patients experiencing high-intensity pain. However, side effects exist and some patients may misuse opioids. To better understand how opioids are prescribed to patients with early-stage cancer and how to enhance opioid safety, clinicians' views of opioid prescribing were explored. METHODS: This was a qualitative inquiry including any Alberta clinician prescribing opioids to patients with early-stage cancer. Semistructured interviews were conducted with nurse practitioners (NP), medical oncologists (MO), radiation oncologists (RO), surgeons (S), primary care physicians (PCP), and palliative care physicians (PC) between June 2021 and March 2022. Interpretive description was used to analyze the data using two coders (C.C. and T.W.). Debriefing sessions were used to resolve and discrepancies. RESULTS: Twenty-four clinicians were interviewed (NP [n = 5], MO [n = 4], RO [n = 4], S [n = 5], PCP [n = 3], and PC [n = 3]). The majority had been in practice at least 10 years. Prescribing practices were related to disciplinary perspective, goals of care, patient condition, and resource availability. Most clinicians did not see opioid misuse as a problem but were aware that specific patient risk factors are present and that long-term use can be problematic. Most clinicians undertake safe prescribing approaches tacitly (eg, screening for past opioid misuse and reviewing number of prescribers) and not all agreed they should be universally applied. Barriers (eg, procedural and time) and facilitators (eg, education) to safe prescribing approaches were identified. CONCLUSION: To enhance uptake and cross-disciplinary consistency of safe prescribing approaches, clinician education regarding opioid misuse and benefits of safe prescribing practices, and addressing procedural barriers are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".