Health Care Practitioners’ Perspectives on Deprescribing Anticholinergic and Sedative Medications in Older Adults
Bibliographic record
Abstract
BACKGROUND: Anticholinergic and sedative (ACh-Sed) medications are commonly prescribed for older adults and are associated with adverse events. OBJECTIVES: (1) To investigate perspectives of health care practitioners (HCPs) surrounding deprescribing (withdrawal) of ACh-Sed medications in older adults; (2) to assess HCPs' perspectives on the design and implementation of a report on a patient's exposure to ACh-Sed medications using the Drug Burden Index (DBI) pharmacological tool. METHODS: This was a qualitative study using focus groups with purposive samples of accredited pharmacists (APs), general practitioners (GPs), and specialist physicians (SPs). Participants were also asked to comment on a sample DBI report of a hypothetical patient and its potential role in practice. The discussions were audiorecorded, transcribed verbatim, and thematically analyzed to derive conceptual domains. QSR NVivo Version 10 was used for data management. RESULTS: Several barriers and enablers to deprescribing ACh-Sed medications in older adults were identified. The most noteworthy barrier to deprescribing related to devolving responsibility. Predominantly, APs expressed frustration surrounding disregard by GPs of their recommendations to deprescribe medications. GPs expressed that deprescribing should be conducted by SPs and vice versa. The DBI report supported and addressed some of the identified barriers to deprescribing ACh-Sed medications in older adults. The HCPs also identified several opportunities and considerations for implementing the DBI report in practice, mainly highlighting that ACh-Sed medications are not the only high-risk medications for older people. CONCLUSIONS: Although HCPs recognize the harms associated with ACh-Sed medication use, they devolve prescribing and management responsibility to other groups of HCPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".