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Record W2119011999 · doi:10.1136/jech-2013-203126.123

PP24 Interventions to Reduce Inappropriate Prescribing of Antipsychotic Medications to People with Dementia Living in Residential Care: A Systematic Review

2013· review· en· W2119011999 on OpenAlexaboutno aff
Jo Thompson Coon, Rebecca Abbott, Morwenna Rogers, Rebecca Whear, Stephen Pearson, Iain Lang, Nick Cartmell, Ken Stein

Bibliographic record

VenueJournal of Epidemiology & Community Health · 2013
Typereview
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLMedicinePsycINFOPsychological interventionDementiaCitation indexMEDLINEAntipsychoticCitationInclusion (mineral)Cochrane LibrarySocial Sciences Citation IndexGerontologyScience Citation IndexPsychiatryFamily medicineAlternative medicineSchizophrenia (object-oriented programming)Library sciencePsychology

Abstract

fetched live from OpenAlex

Background The use of antipsychotic medications in dementia is associated with increased mortality. A significant proportion of long-term antipsychotic use is believed to be inappropriate. The Department of Health National Dementia Strategy recently targeted a two-thirds reduction in antipsychotic usage. The aim of this review is to explore qualitative and quantitative literature to identify interventions that have been used to reduce inappropriate prescribing of antipsychotics and the barriers and enablers to the implementation of these interventions. Methods Potentially relevant papers were identified in the following ways i) electronic searches inMedline, PsycINFO, Embase, AMED, Social Policy and Practice including AgeInfo (OvidSp); CDSR, CENTRAL (Cochrane Library); CINAHL (EBSCOhost); British Nursing Index, AMED (NHS Evidence) and Science Citation Index and Social Science Citation Index (Web of Science) from inception to November 2012, ii) forward and backward citation chases, iii) hand searches of review papers identified in the search, iii) searching of relevant organisations’ websites. All comparative studies were included. Screening of articles for inclusion, data extraction and quality appraisal were performed by one reviewer and checked by a second with discrepancies resolved by discussion with a third if necessary. Results Eleven studies (involving staff and patients in 55 care homes) met the inclusion criteria. All reported quantitative data. Studies were conducted in the UK (n = 4), Canada (n = 2), USA (n = 2) Australia (n = 1) and Norway (n = 1). Studies were of varied design with associated quality issues. No papers in which barriers to the implementation of interventions had been studied were identified. All interventions were unique and involved the delivery of an education or training package (n = 5), the use of in-reach teams in which regular visits by psychiatrists, mental health nurses and psychologists enabled regular patient review in the care home (n = 4), audit followed by feedback and re-audit (n = 1) or regular review, education and the development of a behavioural management approach (n = 1). In all studies, the proportion of residents receiving antipsychotic medication was reduced following the intervention. Conclusion There is some evidence to suggest that simple changes to the way patients are reviewed and monitored and/or enhanced education and training can reduce the prescription rates of antipsychotics for people with dementia living in residential care. In order to guide best practice in this area, further qualitative study is required to explore the barriers and enablers to successful implementation of these interventions. Large rigorous studies with extended post-intervention data collection are also necessary to ascertain the long term impact on prescribing practices.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.008
Bibliometrics0.0110.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.380
GPT teacher head0.553
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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