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Record W2156136085 · doi:10.1177/0733464812440042

Effect of an Interdisciplinary Educational Program on Antipsychotic Prescribing Among Residents With Dementia in Two Long-Term Care Centers

2012· article· en· W2156136085 on OpenAlexafffundabout
Johanne Monette, M. Monette, Nadia Sourial, Alain C. Vandal, Christina Wolfson, Nathalie Champoux, John Fletcher, Maryse Savoie

Bibliographic record

VenueJournal of Applied Gerontology · 2012
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalSte. Anne's HospitalRoyal Victoria HospitalMcGill University Health CentreRoyal Victoria Regional Health CentreJewish General Hospital
FundersUniversité de MontréalMcGill UniversityU.S. Department of Veterans Affairs
KeywordsDementiaAntipsychoticLogistic regressionMedicineLong-term careOddsOdds ratioCenter (category theory)PsychiatryGerontologyFamily medicineSchizophrenia (object-oriented programming)Internal medicine

Abstract

fetched live from OpenAlex

The effect of an educational program on antipsychotic prescribing was assessed in two Canadian long-term care centers (LTCC). In each center (Center A residents, n = 258 and Center B residents, n = 191, with dementia at program inception), the rate of change in the odds of using antipsychotics in residents was estimated using mixed-effects logistic regression during a 6-month program period and a 4-month postprogram period, with baseline proportions of use estimated during the 6 months prior to the program. Preprogram proportions of antipsychotic use were 41.6% and 46.2%, respectively. Antipsychotic use decreased during the program in both centers: (odds ratio with 95% CI: 0.943 per week [0.921, 0.965] and 0.969 per week [0.944, 0.994], respectively). During the postprogram period, antipsychotic use increased in Center A (1.039 per week [1.007, 1.072]) but decreased progressively in Center B. The study results suggest the need to implement an ongoing educational program in LTCC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.398
Teacher spread0.382 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2012
Admission routes3
Has abstractyes

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