What Geriatrics Know about Specific Medications
Bibliographic record
Abstract
The study has aimed to investigate the Medication Knowledge (MK) in elders and identify factors that affect knowledge and the areas that are needed to be enhanced. Moreover, the perception of elders in regards to knowledge provided by healthcare professionals (HCPs) has also been studied. A cross-sectional survey has been performed, which is comprised of elders in ambulatory care settings. A questionnaire about Medication Knowledge Assessment (MKAQ) has been prepared for data collection. Illiteracy, polypharmacy, and multiple clinic follow-ups have been identified as significant factors contributing towards inadequate knowledge. The study revealed that significant number of elders are self-dependent in taking and managing their medicines despite of increased age and multiple medical problems. 73% of the elders were aware about the place to keep their medications and 82% knew about the next date of refill. However, male patients were found to be well-aware about direction of use (P = 0.04) and indications (P = 0.03). Evidence-based approaches individualized to the needs of elders, which are obligatory to be developed for advancing MK. The impact of these interventions should be studied in future on improving knowledge.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".