Development and validation of patient-community pharmacist encounter toolkit regarding substance misuse: Delphi procedure
Bibliographic record
Abstract
BACKGROUND: Pharmacists' roles and services for patients with substance use are not well defined and inconsistent from site to site. Several barriers have been identified that hinder pharmacists' care for people who use substances, such as a lack of training and resources. Clinical practice tools can aid in transferring evidence-based approaches to the practice sphere. OBJECTIVES: The aim of the study was to develop a substance misuse management toolkit for community pharmacists to help them manage their encounters with people who use substances. METHODS: A focused literature review was conducted and 2 needs assessment studies, one for community pharmacists and one for patients informed the development of the toolkit. The toolkit is an adaption of the screening, brief intervention, and referral to treatment (SBIRT) approach, which is one of the most well-defined and effective strategies for substance use management. However, SBIRT is a novel care model in community pharmacy settings. Therefore, a substance misuse management toolkit with 20 items was created for community pharmacists incorporating evidence-based strategies and clinical algorithms. Delphi procedure was used to validate the toolkit. RESULTS: Two rounds of questions were sent to experts in the field of substance misuse, some of whom were pharmacists. In both rounds, these experts were asked to rate the appropriateness and clarity of items in the toolkit and provide comments and suggestions. Items with a median rating of 7 or more out of 10 were included in the toolkit. In the second round, the experts were asked to rerate the revised version and provide additional feedback. After the second round, agreement was reached for almost all items of the toolkit. CONCLUSION: A Delphi procedure was successfully used to provide evidence of the validity of the new guiding toolkit for community pharmacists. The toolkit will be implemented and evaluated to provide additional evidence of validity in practice.
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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 | 0.092 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".