Usage and Costs of Regular Acetaminophen (Paracetamol) in Canadian Long-Term Care Facilities
Bibliographic record
Abstract
Objectives Evidence suggests that the use of regular acetaminophen (paracetamol) in long-term care (LTC) is a low-value intervention, that it does not improve pain or quality of life, and that it has the potential for adverse effects. Our objective was to assess the usage of regular acetaminophen in Canadian LTC facilities as well as the costs associated with its use. Design Cross-sectional study. Setting and Participants Canadian LTC facilities serviced by a national LTC pharmacy provider in 2022. Methods Descriptive statistics were used to characterize prevalence, dosing, dispensation frequency, type, and costs of regular acetaminophen dispensations (cost of tablets, carbon emissions, and nursing dispensation time). Results The data set included 49,419 residents (median age: 86, women: 65%) from British Columbia (5.5%), Alberta (7.1%), Manitoba (23.0%), and Ontario (64.4%). The mean prevalence of regular acetaminophen dispensations was 45.7%. Among residents dispensed regular acetaminophen, 85% of residents were dispensed >1000 mg of acetaminophen/day, the mean defined daily dose per 1000 residents per 1 day was 317 [standard deviation (SD) 56], 59.3% were dispensed acetaminophen ≥3 times per day, and dispensations were approximately evenly split between 325- and 500-mg tablets. The 27.8 million tablets of acetaminophen dispensed in 2022 cost $870,000; had a carbon footprint of 54.8 tonnes of carbon dioxide equivalents (CO 2 e); and required 191,000 nursing hours, the equivalent of 92 nurses working full-time for 1 year. Conclusion and Implications Regular acetaminophen use is highly prevalent in LTC and has substantial costs. It would be advantageous to re-examine acetaminophen use in LTC facilities.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".