PHArmacists’ perspective oN the Take hOme naloxone prograM (The PHANTOM Study)
Bibliographic record
Abstract
Objective: To evaluate pharmacists’ attitudes toward the Take Home Naloxone (THN) program and identify areas that could be improved to support pharmacists’ involvement. Methods: Pharmacists on the Alberta College of Pharmacists’ directory were invited to complete an online survey between July 10 and August 8, 2016. The survey consisted of 19 questions. Descriptive statistics were used to analyze the data. Results: Four hundred seventy pharmacists completed the survey (response rate = 11.2%). A total of 76.8% of respondents strongly agreed or agreed that pharmacists should be screening patients to identify those at risk of opioid overdose. Full-time pharmacists were more likely to agree ( p = 0.02). A total of 79.8% of respondents strongly agreed or agreed that pharmacists should be recommending THN kits. Pharmacists working in large population centres ( p = 0.008) and full-time pharmacists ( p = 0.02) were more likely to agree with this statement. Furthermore, 60.6% of pharmacists were extremely willing or very willing to participate in the THN program. Pharmacists in practice for ≤15 years were more willing to participate in the THN program than pharmacists in practice >15 years ( p = 0.03). The most common perceived barriers to implementation of the THN program were lack of time in pharmacists’ current work environment and education about the program. Conclusions: Overall, pharmacists had positive attitudes toward screening patients to identify those at risk of opioid overdose, recommending THN kits and willingness to participate in the program. Factors that may facilitate increased participation in the program include addressing time issues and improving education about the THN program.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".