MANAGING DISRUPTIVE BEHAVIORS WITH NEUROLEPTICS: Treatment Options for Older Adults in Nursing Homes
Bibliographic record
Abstract
Disruptive behaviors are frequent among elderly individuals in long-term care centers. Neuroleptics remain the most common pharmacological treatment for controlling these challenging behavioral manifestations. However, their effectiveness is a subject of controversy and it is unclear what specific behaviors are more likely to be managed with neuroleptic medications. The objective of this study was to identify the types of disruptive behaviors for which neuroleptics are given to elderly individuals in long-term care facilities and determine if the frequency of these behaviors increases the risk of being prescribed neuroleptics. A cross-sectional study was conducted with 2,332 participants ages 65 or older living in 28 long-term care facilities. Among them, 27.8% had taken at least one neuroleptic drug in the prior week. The administration of neuroleptics was not linked to the presence of any one specific disruptive behavior. However, a significant finding was that the greater the frequency of disruptive behavior exhibited by an elderly individual, the greater the risk of them being administered a neuroleptic medication. A multi-dimensional approach to the assessment of disruptive behaviors is recommended to facilitate the identification of the underlying causes of those behaviors. Accordingly, it is suggested that non-pharmacological treatment plans be adapted to each situation and then implemented to potentially reduce the use of neuroleptics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".