Nurse Practitioners and The Use of Antipsychotic Medications in Long Term Care in Ontario, Canada
Bibliographic record
Abstract
Introduction: Antipsychotic use in Long-Term Care (LTC) in Ontario, Canada continues to pose a challenge in the care of older adult patients living in these institutions. The dangers and patterns of inappropriate prescribing have been documented frequently. Most of the current literature focuses on dementia and behavioral and psychological symptoms of dementia, the role of prescribers, or on interprofessional interventions with person-centered care to address the behavior. Very little discussion has focused on the role of nurse practitioners and other frontline long-term care staff in the assessment and interactions with residents that may result in prescriptions of antipsychotics. Objectives: The purpose of this population based retrospective study of data from all LTC facilities in Ontario, Canada in 2019-2020 was to determine the extent to which antipsychotic medications were used in and the factors associated with this use. Reflections about the NP role are discussed. Results: The results demonstrate that over thirty percent of residents in LTC continue to receive antipsychotics and those with the responsive behaviours are significantly more likely to be prescribed antipsychotics. Conclusions: The findings identify a potential link between over-burdened front-line staff and increased antipsychotic prescriptions, as well as continued use of antipsychotics in attempts to prevent harm to residents and staff at long-term care homes. Recommendations are made that include changes to legislation that will ensure optimal front-line care and time for care, increased training for front line staff and, in particular, how the role of the nurse practitioner in LTC can be utilized to optimize the appropriate use of antipsychotics, and the support of discontinuing or decreasing the dose of antipsychotics when required.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".