The prevalence of suboptimal prescribing of medication in First Nations older adults in the Torres Strait
Bibliographic record
Abstract
OBJECTIVES: This study assessed the prevalence of suboptimal prescribing of medicines in First Nations older adults in the Torres Strait. Types of suboptimal prescribing included polypharmacy, over-prescribing, under-prescribing and anticholinergic burden. It also assessed any significant associations between suboptimal prescribing and common age-related problems such as falls, reduced function and cognition. METHOD: Cross-sectional study (2015-2018) on 18 island and five mainland communities in the Torres Strait and Northern Peninsula Area of Far North Queensland, Australia. Community-dwelling residents aged 45 years and older who identified as Torres Strait Islander and/or Aboriginal with complete medication histories were recruited in this study. Validated prescribing tools were used to identify suboptimal prescribing practises. RESULTS: There were 254 participants with complete medication histories. The mean age was 65.7 (SD ± 10.9, range 45-93), with 65% female. Suboptimal prescribing in this study was 74%. Of these, 49% of participants had polypharmacy, 44% were over-prescribed, and 36% were under-prescribed. Anticholinergic burden was identified in 26% of participants. Polypharmacy was more prevalent in participants who were dependent on instrumental activities of daily living (iADLs). CONCLUSIONS: The results demonstrate the importance of general practitioners, health-care workers or pharmacists, to monitor medication prescribing in this population. Frequent review of medications to reduce suboptimal prescribing practices within these communities may help to reduce adverse outcomes because of prescribing practices.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".