A long-term-care setting pilot study evaluating predictors of success in medication self-administration.
Bibliographic record
Abstract
OBJECTIVE: To determine whether any specific patient variables or a short battery of neuropsychological tests of cognition and memory predict success in medication self-administration. DESIGN: A prospective, single-blinded design. SETTING: An extended care center of a university-affiliated VA Medical Center. PATIENTS: Thirty predominately male, older veteran patients, mean age 70 +/- 4 years with a range of 63 to 79 years. INTERVENTIONS: Neuropsychological testing [Mini-Mental Status Examination (MMSE), Delayed Word Recall Test (DWRT), Shipley Institute of Living Scale (SILS), Neurobehavioral Cognitive Status Examination (NCSE), Hopkins Verbal Learning Test (HVLT)], twice a week unannounced bedside medication counts and medication administration record inspections, and educational instruction, if needed, by nurses and pharmacists. MAIN OUTCOME MEASURES: Patient characteristics such as age, number of medications, presence of a disorder that can alter cognitive or memory function, years of education, and results from the above listed neuropsychological tests. The dependent variable was successful or not successful as defined by whether the patient required a re-education intervention. RESULTS: Fifteen patients required one or more re-education intervention(s) as a result of meeting the criteria for not being successful. The absence of major depression, stroke, or anxiety disorder did tend to predict success (P = 0.0716) in medication self-administration. The other patient specific characteristics did not predict success. Among the neuropsychological tests administered, only the Judgment Subtest of the NCSE tended to predict success (P = 0.098). The MMSE, DWRT, SILS,HVLT tests did not predict successful performance in the self-medication program. CONCLUSIONS: Although the presence of a diagnosis that could potentially alter cognitive and memory function tended to predict success, no patient characteristics were found that predicted success independently. Among the neuropsychological tests, only the Judgment subtest of the NCSE tended to predict success in medication self-administration. We conclude that the NCSE and characterization of patient-specific factors, including diseases that may affect cognitive and memory function, seem to be the best predictors of success in medication self-administration in a long-term-care setting.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".