Educational needs, practice patterns and quality indicators to improve geriatric pharmacy care
Bibliographic record
Abstract
BACKGROUND: As the population ages and pressure increases to reduce adverse drug reactions and drug-related hospitalizations in the elderly, there will be a growing demand for pharmacists to competently take on shared responsibility for effective and safe prescribing in older adults. METHODS: A cross-sectional postal survey was distributed to 3927 hospital and community pharmacists across Québec about their educational needs and practice patterns in geriatric care. Perceptions of different quality performance indicators were sought. Modifiable factors associated with higher performance were determined using univariate logistic regression. RESULTS: Seven hundred six pharmacists (18%) completed the survey. Less than 50% were aware of the prevalence of polypharmacy, inappropriate prescribing, drug-related hospitalizations or falls in the geriatric population. Forty-one percent of community pharmacists and 74% of hospital pharmacists acknowledged familiarity with the Beers criteria of drugs to avoid in the elderly. The likelihood of screening for inappropriate prescriptions was 2.96 (95% confidence interval = 1.97-4.47) among pharmacists familiar with the Beers criteria and 2.24 (95% confidence interval = 1.50-3.34) among those who received continuing geriatric education in the workplace. On average, pharmacists reported having time to conduct detailed medication reviews in 30% of their older patients. The 2 quality indicators of geriatric care that were ranked most pertinent were being able to track the number of patients requiring hospitalization for drug-related problems and monitoring rates of inappropriate prescriptions. Ninety-six percent of respondents desired continuing education about geriatric care. CONCLUSION: Exposure to continuing education in geriatric pharmacotherapy in the workplace is the most consistent determinant of professional performance to improve drug outcomes in the elderly.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".