Neuroleptic and benzodiazepine use in long-term care in urban and rural Alberta: characteristics and results of an education intervention to ensure appropriate use
Bibliographic record
Abstract
OBJECTIVES: To examine the use of psychotropic drugs in 24 rural and urban long-term care (LTC) facilities, and compare the effect of an education intervention for LTC staff and family members on the use of psychotropic drugs in intervention versus control facilities. METHODS: Interrupted time series with a non-equivalent no-treatment control group time series. Data on drug use were collected in 24 Western Canadian LTC facilities (10 urban, 14 rural) for three 2-month time periods before and after the intervention. Pharmacy records were used to collect data on drug, class of drug, dose, administration, and start/stop dates. Chart reviews provided demographics, pro re nata (prn) use, and indications for drug use. Subjects comprised 2443 residents living in the 24 LTC facilities during the 1-year study. An average of 796.33 residents (32.7%) received a psychotropic drug. An education intervention on psychotropic drug use in LTC was offered to intervention physicians, nursing staff, pharmacists and family members. RESULTS: Approximately one-third of residents received a psychotropic drug during the study, often for considerable lengths of time. A minority of psychotropic drug prescriptions had a documented reason for their use, and 69.5% of the reasons would be inappropriate under Omnibus Budget Reconciliation Act (OBRA) legislation. Few psychotropic drug prescriptions were discontinued or reduced during the study. More urban LTC residents received neuroleptics and benzodiazepines than their rural counterparts (26.1% vs. 15.7%, and 18.0% vs. 7.6%, respectively). The education intervention did not result in any significant decline in the use of these drugs in intervention facilities. CONCLUSION: The results suggest substantial use of psychotropic drugs in LTC, although rural LTC residents received approximately half the number of psychotropic drugs compared with urban residents. A resource-intensive intervention did not significantly decrease the use of psychotropics. There is a need for better monitoring of psychotropic drugs in LTC, particularly given that voluntary educational efforts alone may be ineffective agents of change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".