Clinical Pharmacists’ Knowledge of and Attitudes toward Older Adults
Bibliographic record
Abstract
Background: Although pharmacy literature suggests that pharmacists have a positive attitude towards older adults, there is a paucity of studies that have measured pharmacists’ knowledge or attitudes towards older people. The purpose of our study was to assess the knowledge and attitudes of hospital pharmacists toward older adults. Methods: An electronic survey was distributed over two months to clinical hospital pharmacists across the province of Alberta, Canada. The survey was composed of two validated tools, the Palmore Facts of Aging Quiz (PFAQ) and Kogan’s Attitude toward Old People Scale (KOPS). PFAQ is scored from 0 (poor knowledge) to 25 (high knowledge) and KOPS from 34 to 204, with higher than 119 indicating a positive attitude. Results: A total of 153 pharmacists completed the survey (response rate of 24%). The mean age was 39 (SD 10.8) years; the average years practiced was 15 (SD 11), and the majority of respondents (n = 65) reported that >50% of patients in their practice were geriatrics. The mean correct responses on the PFAQ were 18.8 (SD 2.6). KOPS had a mean score of 156.8 (SD 14), with only one pharmacist score falling below 119, indicating a negative attitude. There was a statistically significant, positive correlation between attitudes and knowledge (r = 0.38, p < 0.05), as well as the increasing age of the respondents (r = 0.18, p = 0.03). The remaining measured categories (i.e., gender, years of pharmacy practice) had no significant effect. Conclusion: Clinical hospital pharmacists in Alberta have a positive attitude toward geriatric patients, as well as a satisfactory knowledge of older adults.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".