RN Evaluation of Errorless Methods in Teaching Discharge Medications to Cognitively Challenged Patients
Bibliographic record
Abstract
PURPOSE: To identify (1) effectiveness of current registered nurse (RN) strategies in teaching discharge medications to cognitively challenged patients and (2) whether errorless teaching/learning (ETL) with pictorial medication cards improves such instruction. DESIGN: Cross-sectional, qualitative, pretest/posttest. METHODS: Open-ended interviews and a class on ETL were conducted with a purposive sample of 10 expert staff RNs from rehabilitation and neurological telemetry units in a 377-bed, not-for-profit hospital. Data were analyzed using content analysis. FINDINGS: Informants reported current practices that were not adapted for the cognitively challenged population (n = 10). They also found the new ETL easy, effective, and useful in promoting safety and satisfaction but reported that writing on the cards was too time-consuming (n = 7). CONCLUSIONS: Although not generalizable, outcomes suggest value in revising and evaluating ETL with a pictorial card for teaching this population. CLINICAL RELEVANCE: Discharge medication knowledge is critical to safe self-management, and using ETL with cognitively challenged persons may promote learning.
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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 | 0.003 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".