Should Health Care Aides Assist With Medications in Long-Term Care?
Bibliographic record
Abstract
Objective: The objective of the study was to determine whether health care aides (HCAs) could safely assist in medication administration in long-term care (LTC). Method: We obtained medication error reports from LTC facilities that involve HCAs in oral medication assistance and we analyzed Resident Assessment Instrument (RAI) data from these facilities. Standard ratings of error severity were “no apparent harm,” “minimum harm,” and “moderate harm.” Results: We retrieved error reports from two LTC facilities with 220 errors reported by all health care providers including HCAs. HCAs were involved in 137 (63%) errors, licensed practical nurses (LPNs)/registered nurses (RNs) in 77 (35%), and pharmacy in four (2%). The analysis of error severity showed that HCAs were significantly less likely to cause errors of moderate severity than other nursing staff (2% vs. 7%, chi-square = 5.1, p value = .04). Conclusion: HCAs’ assistance in oral medications in LTC facilities appears to be safe when provided under the medication assistance guidelines.
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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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".