Drug-Related Problems Identified in a Workplace Asthma Self-Management Program
Bibliographic record
Abstract
Background/Objective: Drug-related problems (DRPs) commonly lead to many adverse outcomes in patients with asthma. The objective of this study was to identify DRPs, potential DRPs, and non-DRPs in a workplace asthma self-management program. Methods: “Inspire at Work” is a unique workplace asthma self-management program that is a partnership between Medavie Blue Cross (a benefits carrier) and 7 large employers in New Brunswick. As part of this program, a certified asthma educator conducted 4 in-depth assessments for each participant. The reports from these assessments were independently reviewed by a pharmacist-researcher to classify the drug-related problems, potential DRPs, and non-DRPs identified by the asthma educator. Results: Ninety-nine patients completed the Inspire at Work program, and 46 DRPs were identified in 34 patients. The most common types of DRPs were untreated indications (20 patients), failure to receive drugs (12 patients), and subtherapeutic dosage (11 patients). A total of 188 problems were found — an average of 1.90 problems per patient. Conclusion: A significant number of problems in the care of patients with asthma were identified as part of this workplace asthma self-management program. Further stages in this study will examine the relationship of these problems to adverse clinical outcomes and the utilization of health care resources.
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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 | 0.001 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".