Accuracy of Inpatient Recall of Interaction With a Pharmacist: A Validation Study From 2 Acute Care Teaching Hospitals
Bibliographic record
Abstract
BACKGROUND: Research has shown that inpatients may not accurately report interacting with a pharmacist. OBJECTIVE: To determine accuracy of patients' recollection of meeting with a pharmacist at 2 acute care teaching hospitals in Edmonton, Alberta, Canada. METHODS: Retrospective review of 391 surveyed patients discharged from April 2013 to March 2014. Responses to meeting a pharmacist (yes/no) were compared with 2 reference standards: pharmacist documentation in patient charts and pharmacist clinical workload data. Sensitivity, specificity, positive predictive, and negative predictive values were calculated. RESULTS: One hundred ninety-five (49.9%) respondents reported meeting with a pharmacist. Of these, 71 (36.4%) had corresponding pharmacist chart documentation. Of the 196 respondents who reported not speaking with a pharmacist, 73 (37.2%) had documentation present. Compared with patient charts, sensitivity and specificity were 49.3% and 49.8%, respectively. Positive and negative predictive values were 36.4% and 62.8%, respectively. Similar results were seen in comparison with the workload data. CONCLUSIONS: Patients often inaccurately reported meeting with a pharmacist in the acute care setting. The results are useful for pharmacist training, patient education, and for refinement of the current survey question.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".