Nursing Time Devoted to Medication Administration in Long‐Term Care: Clinical, Safety, and Resource Implications
Bibliographic record
Abstract
OBJECTIVES: To quantify the time required for nurses to complete the medication administration process in long-term care (LTC). DESIGN: Time-motion methods were used to time all steps in the medication administration process. SETTING: LTC units that differed according to case mix (physical support, behavioral care, dementia care, and continuing care) in a single facility in Ontario, Canada. PARTICIPANTS: Regular and temporary nurses who agreed to be observed. MEASUREMENTS: Seven predefined steps, interruptions, and total time required for the medication administration process were timed using a personal digital assistant. RESULTS: One hundred forty-one medication rounds were observed. Total time estimates were standardized to 20 beds to facilitate comparisons. For a single medication administration process, the average total time was 62.0+/-4.9 minutes per 20 residents on physical support units, 84.0+/-4.5 minutes per 20 residents on behavioral care units, and 70.0+/-4.9 minutes per 20 residents on dementia care units. Regular nurses took an average of 68.0+/-4.9 minutes per 20 residents to complete the medication administration process, and temporary nurses took an average of 90.0+/-5.4 minutes per 20 residents. On continuing care units, which are organized differently because of the greater severity of residents' needs, the medication administration process took 9.6+/-3.2 minutes per resident. Interruptions occurred in 79% of observations and accounted for 11.5% of the medication administration process. CONCLUSION: Time requirements for the medication administration process are substantial in LTC and are compounded when nurses are unfamiliar with residents. Interruptions are a major problem, potentially affecting the efficiency, quality, and safety of this process.
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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 | 0.001 |
| 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".