Managing opioids and mitigating opioid risks in patients with cancer: An environmental scan of the attitudes, confidence, and practices of ambulatory, community and hospital pharmacists practicing in Canada
Bibliographic record
Abstract
INTRODUCTION: Canada is in the midst of an overdose crisis. The use of prescription opioids in Canada has increased steadily over the past two decades, with stark increases in opioid-induced respiratory depression and related deaths. Opioids are the mainstay of treatment for cancer-related pain. Patients with cancer are not immune to the risks associated with opioid use but are underrepresented in available literature outlining risk mitigation strategies. Pharmacists are ideally placed to employ opioid risk mitigation practices to support safe and effective opioid use for patients with cancer-related pain. However, the current attitudes, confidence, and safety practices of pharmacists around how to best support these patients are not known. METHODS: This study was a descriptive environmental scan of pharmacists who provide direct patient care in Canada. An electronic questionnaire was built using the web based Opinio software. It was distributed via email by several provincial and national pharmacy organizations and online platforms. The questionnaire consisted of Likert-scale and open-ended questions and was open to participants for a 6-week period from February 12th to March 23rd, 2020. Analysis was conducted using descriptive statistics and qualitative content analysis. RESULTS: Eighty-one responses from pharmacists in nine provinces were included in the analysis. Respondents endorsed limited and varied practices when caring for patients receiving opioids for cancer-related pain. Further, they demonstrated wide ranging confidence and attitudes regarding opioid risk mitigation practices and beliefs. Less than 50% of pharmacists were aware of resources available for their patients with non-medical opioid use, and/or patients at high risk of opioid-induced respiratory depression. Education, resources, and communication were the most commonly reported perceived facilitators and barriers to resource use. CONCLUSIONS: Pharmacists in Canada report employing opioid risk mitigation practices with low and varied frequency when caring for patients receiving opioids for cancer-related pain. They endorsed varied confidence and limited awareness of available provider and patient resources. These findings may help inform the development of new education models and evidence-based guidelines. New education models and evidence-based guidelines will support pharmacists in their pharmaceutical care of this vulnerable patient population, ultimately aiming to improve patient outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".