Managing Opioids and Mitigating Risk: A Survey of Attitudes, Confidence and Practices of Oncology Health Care Professionals
Bibliographic record
Abstract
In response to Canada's opioid crisis, national strategies and guidelines have been developed but primarily focus on opioid use for chronic noncancer pain. Despite the well-established utility of opioids in cancer care, and the growing emphasis on early palliative care, little attention has been paid to opioid risk in this population, where evidence increasingly shows a higher risk of opioid-related harms than was previously thought. The primary objective of this study was to assess oncology clinicians' attitudes, confidence, and practices in managing opioids in outpatients with cancer. This was explored using pilot-tested, profession-specific surveys for physicians/nurse practitioners, nurses and pharmacists. Descriptive analyses were conducted in aggregate and separately based on discipline. Univariate and multiple linear regression analyses were performed to explore relationships between confidence and practices within and across disciplines. The survey was distributed to approximately 400 clinicians in January 2019. Sixty-five responses (27 physicians/nurse practitioners, 31 nurses, 7 pharmacists) were received. Participants endorsed low confidence, differing attitudes, and limited and varied practice in managing and mitigating opioid risks in the cancer population. This study provides valuable insights into knowledge gaps and clinical practices of oncology healthcare professionals in managing opioids and mitigating associated risks for patients with cancer.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".