Improving Patient and Caregiver New Medication Education Using an Innovative Teach-back Toolkit
Bibliographic record
Abstract
BACKGROUND: Patients and caregivers are often not adequately informed about new medications. Nurses can lead innovations that improve new medication education. LOCAL PROBLEM: Healthcare Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores on medication questions trailed state and national levels in one Midwestern hospital. METHODS: This quality improvement project, guided by the Ottawa Model of Research Use and the Always Use Teach-back! innovative toolkit, used a 1-group pre- and posteducation design with RNs, patients, and caregivers. INTERVENTION: RNs (n = 25) were observed in patient/caregiver education and surveyed in confidence/con-viction in the teach-back method before and after education. Patients' (n = 74) and caregivers' (n = 33) knowledge was assessed. RESULTS: RNs reported significant increases in conviction in the importance of (P < .0001), confidence in using (P < .0001), and frequency in using (P < .0001) teach-back. With teach-back, both patients and caregivers recalled the purpose and side effects of new medications. Specific HCAHPS scores increased from 6% to 10%. CONCLUSION: The teach-back method strengthened safe nursing practice and enhanced quality in new medication education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".