Bibliographic record
Abstract
The evidence-informed consensus statements were developed by a panel of experts and knowledgeable individuals, through a consensus-building process from June 2024 to January 2025. The target for long-term care (LTC) homes in Canada reflects a level of excellence for the “potentially inappropriate use of antipsychotics in long-term care” quality indicator to which LTC homes can aspire, and represents acceptable practice in Canada. The annual improvement goal supports collective action toward the target by giving LTC homes something to strive for each year regardless of their proximity to the target rate for Canada. The target for LTC homes in Canada and the annual improvement goal are meant to inspire change to improve the health and safety of people living in LTC by setting clear expectations of where Canada should be regarding the appropriate use of antipsychotics in LTC. They are intended to be ambitious, realistic, and encouraging for LTC homes, but they are not mandatory for LTC homes. Consensus Statement 1: The Target for LTC Homes in Canada The panel recommends 15% as the target for LTC homes in Canada for the “potentially inappropriate use of antipsychotics in long-term care” quality indicator. Note: The target sets a standard for the overall risk-adjusted rate for Canada. It is a level of excellence for the quality indicator to which LTC homes can aspire. There is no time frame associated with the target. This means that the proportion of people living in LTC homes across Canada receiving antipsychotic drugs without a diagnosis of psychosis should ideally be 15% or less. All LTC homes can contribute to reaching the target for the country through quality improvement initiatives that reduce potentially inappropriate antipsychotic use in their setting to 15% or lower. Consensus Statement 2: Annual Improvement Goal For LTC homes that are not meeting the target for LTC homes in Canada, the panel recommends a 15% relative reduction as the annual improvement goal for the “potentially inappropriate use of antipsychotics in long-term care” quality indicator. This means that these LTC homes should aim to reduce the proportion of people living there who are receiving antipsychotic drugs without a diagnosis of psychosis by 15%, relative to the proportion from the previous year. For example, if an LTC home with a rate of 20% for the quality indicator were to achieve the 15% relative reduction over 1 year, it would mean that their new rate would be 17%.
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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 | 0.126 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".