A Prescription for misunderstanding: Opportunities for misinterpretation along the information flow from physician to patient
Bibliographic record
Abstract
Background: Medication errors are costly in both human and financial loss. Interpretations of prescription instructions have been found to vary considerably, including discrepancies between patients and prescribing physicians. Objective: Identify discrepancies in interpretation of prescription instructions between healthcare consumers, nurses, and physicians Method: Research Design: Cross-sectional study; Setting: Large university, 2 hospitals and various clinics in Mississippi and Florida; Participants: 74 young healthcare consumers, 34 RNs, and 36 physicians; Measures: Questionnaires asking for interpretations (i.e., what times would/should you take the drug) for various prescription instructions were provided to healthcare consumers, nurses, and physicians. Results: There was considerable within-group variability in the interpretation of prescription instructions by all groups including physicians. Moreover, physicians, nurses, and healthcare consumers exhibited between group variability in their interpretation of prescription instructions. None of the instructions were uniformly interpreted and a fair number of consumer and nurse interpretations resulted in potentially unsafe schedules of drug administration. Some physicians and nurses also apparently lacked awareness of the potential for interpretation variability. Conclusions: Because two healthcare providers can have different intentions for identical instructions, an awareness and subsequent education of potential sources of misinterpretation is vital. The present results indicate a need to identify and explore within-group variability of intent among physicians and other healthcare providers.
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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 | 0.018 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".