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Record W2312965551 · doi:10.1017/s1041610216000338

A biopsychosocial vignette for case conceptualization in dementia (VIG-Dem): development and pilot study

2016· article· en· W2312965551 on OpenAlexfundno aff
Aimee Spector, Molly Hebditch, Charlotte R. Stoner, Luke Gibbor

Bibliographic record

VenueInternational Psychogeriatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsBiopsychosocial modelDementiaConceptualizationPsychologyVignetteReliability (semiconductor)Clinical psychologyApplied psychologyMedicinePsychotherapistSocial psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: The ability to identify biological, social, and psychological issues for people with dementia is an important skill for healthcare professionals. Therefore, valid and reliable measures are needed to assess this ability. This study involves the development of a vignette style measure to capture the extent to which health professionals use "Biopsychosocial" thinking in dementia care (VIG-Dem), based on the framework of the model developed by Spector and Orrell (2010). METHODS: The development process consisted of Phase 1: Developing and refining the vignettes; Phase 2: Field testing (N = 9), and Phase 3: A pilot study to assess reliability and validity (N = 131). RESULTS: The VIG-Dem, consisting of two vignettes with open-ended questions and a standardized scoring scheme, was developed. Evidence for the good inter-rater reliability, convergent validity, and test-retest reliability were established. CONCLUSIONS: The VIG-Dem has good psychometric properties and may provide a useful tool in dementia care research and practice.

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)0.000
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 score0.067
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.039
GPT teacher head0.369
Teacher spread0.330 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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