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Record W4402580496 · doi:10.1177/23337214241274883

The Wellbeing Index for Persons with Dementia—An Observational Study Based on the Group Observational Measurement of Engagement (GOME)

2024· article· en· W4402580496 on OpenAlexaboutno aff
Jiska Cohen‐Mansfield, Rinat Cohen

Bibliographic record

VenueGerontology and Geriatric Medicine · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
FundersMinerva Foundation
KeywordsObservational studyDementiaIndex (typography)PsychologyGroup (periodic table)Clinical psychologyMedicineGerontologyPsychiatryInternal medicineDiseaseComputer sciencePhysics

Abstract

fetched live from OpenAlex

The Group Observational Measurement of Engagement (GOME) was developed to capture the impact of group recreational activities on the engagement and general wellbeing of persons with dementia. The psychometric properties of the GOME were originally described in a study of group activities conducted at one large Canadian geriatric center. Continuing this work in Israel, this article reports on further psychometric properties of the GOME based on observations of 115 persons with dementia from 10 geriatric units, of which four were senior day center units (in three institutions) and six were nursing units (representing five other institutions). Very good inter-rater reliability between research observers was found. Factor analysis suggests that the GOME's four individual-level outcomes can be combined into one indicator, the Wellbeing Index. Validity, examined via agreement between research observers and group activity leaders who were staff members in the facilities where the group activities were conducted, also showed high levels of positive correlations. The GOME provides a practical tool for assessing wellbeing in the context of group activities. It can be useful in clarifying the relative impact of process variables on participants' general wellbeing.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.284
GPT teacher head0.419
Teacher spread0.135 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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