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Record W4200056068 · doi:10.1093/geroni/igab046.1083

Barriers to and Facilitators for Optimal Intervention for Apathy in Older Adults With Dementia

2021· article· en· W4200056068 on OpenAlexaff
Aderonke Agboji, Shannon Freeman

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsApathyDementiaIntervention (counseling)GuidelinePsychologyHealth professionalsMedicineHealth carePsychiatryClinical psychologyCognitionDisease

Abstract

fetched live from OpenAlex

Abstract Apathy is a persistent symptom in brain disorders. It affects 84% of people with brain disorders. Those affected are more than two times likely to die early than those without. Yet it is often ignored and undertreated. An integrative review guided by Whittemore and Knafl's (2005) framework was carried out to identify the factors that inhibit or facilitate the diagnosis and management of apathy among older adults with dementia. The result of the findings revealed five barriers and three facilitators. Barriers included inconsistencies in the definition and diagnostic criteria, lack of awareness, overlap with other neuropsychiatric disorders, a paucity of evidence-based information, and lack of familiarity. Facilitators were standardized definition and assessment tools, good communication among the interdisciplinary team, and adequate training, education, and experience. In conclusion, efforts should be geared towards raising awareness and developing a practice guideline to aid healthcare professionals in detecting and managing apathy optimally.

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)0.024
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.070
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.324
Teacher spread0.311 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

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