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Record W2543322633

An analysis of community-based nonpharmacological treatments for the behavioural and psychological symptoms of dementia, as alternatives to antipsychotic medication in aging populations around the world

2015· article· en· W2543322633 on OpenAlexaff
Manpreet Lamba

Bibliographic record

VenueGlobal Health: Annual Review · 2015
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPsychological interventionDementiaCINAHLMedicinePsychiatryAntipsychoticPopulationPsychologyGerontologySchizophrenia (object-oriented programming)DiseaseEnvironmental health
DOInot available

Abstract

fetched live from OpenAlex

Background :  Due to global population aging, dementia impacts millions worldwide, and costs billions of dollars annually (2). The adverse events associated with antipsychotic medications, commonly used to manage the behavioural and psychological symptoms of dementia (BPSD), are not negligible and non-pharmacological interventions must be explored (3). Objective/Methods :  This scoping review answers: What are the recent trends in different countries of community- based nonpharmacological interventions as alternatives to antipsychotic medication for BPSD, and what is efficacy of these treatments? CINAHL and OVID were searched, and the articles were analyzed Results :  Four studies use artistic interventions (5,6,7,8), four use technological interventions (9,10,11,12), and three focus on education and behavior change (13,14,15). Analysis :  A SWOT analysis indicates variable sample populations, countries of origin, interventions and efficacy within these studies.  Conclusion :  It is necessary to prioritize research on community-based nonpharmacological interventions for BPSD that promote international collaboration, utilize more diverse populations, and that are promising for use in low-resource settings.

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 score0.207
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.229
GPT teacher head0.548
Teacher spread0.320 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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