An evaluation of Alberta pharmacists’ practices, views and confidence regarding prescription drug abuse and addiction within their practice setting
Bibliographic record
Abstract
Background: Pharmacists play an important role in managing patients with prescription drug abuse and addiction (PDAA). The objective of this study was to explore Alberta pharmacists’ practices, views and confidence in the management of patients at risk of or living with PDAA in their practice setting. Methods: A 26-question online questionnaire was distributed to 4261 pharmacists across Alberta, of whom 656 (15%) participated. The questionnaire consisted of 17 multiple-choice, 6 multipart and 3 free-response questions. Questionnaire responses were collected and analyzed in Qualtrics. Results: Sixty-six percent ( n = 408) of pharmacists indicated that PDAA was prevalent in their practice setting, with 55% ( n = 340) of respondents encountering more than 6 patients with suspected or known PDAA a month. Thirty-five percent ( n = 198) of pharmacists indicated they were moderately confident at identifying patients with potential PDAA. However, 41% ( n = 235) of the pharmacists indicated that they only discuss PDAA with identified patients less than half of the time. Pharmacists lacked confidence in their ability to discuss PDAA treatment options with their patients as well as collaborate with addiction treatment facilities. Lack of training or knowledge in PDAA (48%) and uncertainty of how to initiate discussion or effectively communicate with patients about PDAA (39%) were identified as barriers that significantly or very significantly hindered respondents from managing PDAA in their practice. Conclusions: Although many pharmacists are moderately confident in identifying patients with potential PDAA, several barriers hinder intervention. Providing pharmacists with additional training and resources may better equip them to manage PDAA within their practice settings. Can Pharm J (Ott) 2019;152:xx-xx.
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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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".