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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 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)0.014
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 source (direct Gemma or distilled Codex), 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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