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Record W3202251821 · doi:10.1002/nop2.1042

A nursing mentoring programme on non‐pharmacological interventions against BPSD: Effectiveness and use of antipsychotics—A retrospective, before–after study

2021· article· en· W3202251821 on OpenAlexafffundabout
Roxane Plante‐Lepage, Philippe Voyer, Pierre‐Hugues Carmichael, Edeltraut Kröger

Bibliographic record

VenueNursing Open · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsQuebec Network for Research on AgingUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMedicineDementiaPsychological interventionAntipsychoticIntervention (counseling)NeurocognitivePsychiatryRetrospective cohort studyNursingSchizophrenia (object-oriented programming)CognitionInternal medicine

Abstract

fetched live from OpenAlex

Behavioural and psychological symptoms of dementia (BPSD) are common and have significant implications for patients and caregivers. Non-pharmacological interventions (NPI) have shown to be effective in the management of BPSD. However, the use of antipsychotics to treat BPSD remains ubiquitous. This retrospective, before-after study aimed to examine whether a nurse mentoring programme promoting NPI for BPSD management had a significant association with the use of antipsychotics in older adults with major neurocognitive disorders residing in different settings. Results obtained from the medical files of 134 older adults having benefitted from the mentoring programme demonstrate that this intervention significantly reduced BPSD. The effect on antipsychotics use was modest: a 10% reduction in the use of antipsychotics has been observed among patients for which the NPI were effective. However, the use of antipsychotics remained widespread despite the nursing recommendations of the mentoring team of the Center of Excellence on Aging in Quebec (CEVQ).

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 score0.175
Threshold uncertainty score0.623

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 head0.087
GPT teacher head0.451
Teacher spread0.363 · 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

Citations7
Published2021
Admission routes3
Has abstractyes

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