A long-term care center interdisciplinary education program for antipsychotic use in dementia: program update five years later
Bibliographic record
Abstract
BACKGROUND: While antipsychotic (AP) medications are frequently used in long-term care, current evidence suggests that the risks may offset the benefits, necessitating periodic reassessment of their use. The aims of this present study were: (1) to assess rates of AP use five years after our first intervention to determine the long-term impact; and (2) to implement an updated AP reduction educational intervention program at the same center five years later in order to determine whether AP use could be further reduced. METHODS: Participants were residents with dementia receiving AP medication. The educational program component included separate lectures on pharmacologic and non-pharmacologic treatment of behavioral and psychological symptoms of dementia (BPSD). Completion of the Nursing Home Behavior Problems Scale (NHBPS), physician interviews concerning AP treatment plans for subjects with dementia, and AP administration and dose assessment occurred both at baseline and again between four to five months after the educational program. RESULTS: Of 308 long-term residents with dementia, 53 (17.2%) were receiving regular APs, primarily for agitation, aggressivity, other behavioral problems and psychosis. Of these, six died and one was transferred, leaving 46 participants. At five months, ten (21.7%) residents were no longer receiving APs and seven (15.2%) were on a lower dose; thus, 17 (37.0%) were either discontinued or on a lower dose. There was no worsening of NHBPS scores. CONCLUSION: Despite the low prevalence (17.2%) of AP users at the beginning of the current study compared to that observed five years prior (30.5%), it is still possible to further decrease the proportion of users.
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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 | 0.001 | 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".