Variation and appropriateness of antipsychotic use in long-term care facilities across Newfoundland and Labrador
Bibliographic record
Abstract
OBJECTIVE: The use of antipsychotics to treat seniors in long-term care facilities (LTCFs) has raised concern because of health consequences (i.e., increased risk of falls, stroke, death) in this vulnerable population. This study measured geographic patterns of antipsychotic utilization among seniors living in LTCFs in Newfoundland and Labrador (NL) and assessed potential inappropriateness. METHOD: We analyzed prescription records among adults 66 years and older with provincial prescription drug coverage admitted to LTCFs in NL between April 1, 2011, and March 31, 2014. Patterns of use were analyzed across the 4 regional health authorities (RHAs) in NL and LTCFs. Logistic, Poisson and linear regression models were used to test variations in prevalence, rate and volume of antipsychotic utilization. To assess potential inappropriateness of antipsychotic use, we analyzed data from Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 forms from NL LTCFs between January 1, 2016, and December 31, 2018. Pearson chi-squared analysis was performed at the RHA and LTCF levels to determine changes in percentage of total prescriptions or antipsychotic prescriptions without psychosis. RESULTS: Between 2011 and 2014, 2843 seniors were admitted to LTCFs across NL; of these, 1323 residents were prescribed 1 or more antipsychotics. Within the 3-year period, the percentage of antipsychotic use across facilities ranged from 35% to 78%. Using data from 27,260 RAI-MDS 2.0 assessments between 2016 and 2018, 71% (6995/9851) of antipsychotic prescriptions were potentially inappropriate. DISCUSSION: There is substantial variation across NL regions concerning the utilization of antipsychotics for senior in LTCFs. Facility size and management styles may be reasons for this. CONCLUSION: 2021;154:xx-xx.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".