Neuroleptic drug use in long-term care: An inappropriate panacea?
Bibliographic record
Abstract
Despite the increasing evidence about the inappropriate use of medications by older people, there is very little published evidence about the control and monitoring of neuroleptic drugs used in nursing homes. As others have indicated, this is all the more worrying when set in the context of the paucity of research on nursing home care and the trend to replace registered nurses with untrained care assistants. In the United States, legislation in the form of the Nursing Home Reform Act (OBRA 1987) was introduced, in part, to regulate the prescribing and administration of neuroleptic (antipsychotic) drugs. No such legislation exists in Canada or the United Kingdom. In the case of the latter jurisdiction, the recent Royal Commission on Long-Term Care for older people (The Stationery Office, 1999) has recommended a national care commission to monitor care, and set assessment and quality benchmarks. In Canada this debate has not even begun, and the purpose of this paper is not to ignite controversy, but to raise questions about the use of these drugs with nursing home residents. Voluntary guidelines and education of physicians, nurses and care attendants would be infinitely better than legislation. In the meantime, we need research to address the following questions: For what reasons should these drugs be given to older people? Are these drugs being used appropriately? Is the risk of side-effects too great with these drugs? Are the numbers and type of staff employed in nursing homes adequate/qualified to detect and report side-effects? How well do these drugs manage the behaviours they are given to control? Are they being used as chemical restraints or to make the older person compliant? Are the so-called ‘atypical’ neuroleptic drugs any better? What we offer in this article is background information that might encourage others to not only review their practice but also to address these questions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".