The dispensing of psychotropic medicines to older people before and after they enter residential aged care
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of psychotropic medicine dispensing before and after older people enter residential care. DESIGN: Retrospective national cohort study; analysis of Registry of Senior Australians (ROSA) data. SETTING, PARTICIPANTS: All concession card-holding residents of government-subsidised residential aged care facilities in Australia who entered residential care for at least three months between 1 April 2008 and 30 June 2015. MAIN OUTCOME MEASURES: Proportions of residents dispensed antipsychotic, benzodiazepine, or antidepressant medicines during the year preceding and the year after commencing residential care, by quarter. RESULTS: Of 322 120 included aged care residents, 68 483 received at least one antipsychotic (21.3%; 95% CI, 21.1-21.4%), 98 315 at least one benzodiazepine (30.5%; 95% CI, 30.4-30.7%), and 122 224 residents at least one antidepressant (37.9%; 95% CI, 37.8-38.1%) during their first three months of residential care; 31 326 of those dispensed antipsychotics (45.7%), 38 529 of those dispensed benzodiazepines (39.2%), and 25 259 residents dispensed antidepressants (19.8%) had not received them in the year preceding their entry into care. During the first three months of residential care, the prevalence of antipsychotic (prevalence ratio [PR], 3.37; 95% CI, 3.31-3.43) and antidepressant dispensing (PR, 1.05; 95% CI, 1.04-1.07) were each higher for residents with than for those without dementia; benzodiazepine dispensing was similar for both groups (PR, 1.01; 95% CI, 0.99-1.02). CONCLUSIONS: Dispensing of psychotropic medicines to older Australians is high before they enter residential care but increases markedly soon after entry into care. Non-pharmacological behavioural management strategies are important for limiting the prescribing of psychotropic medicines for older people in the community or in residential care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".