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Record W2018106512 · doi:10.1177/1471301207075640

Measuring family perceived involvement in individualized long-term care

2007· article· en· W2018106512 on OpenAlexafffund
R. Colin Reid, Neena L. Chappell, Jessica A. Gish

Bibliographic record

VenueDementia · 2007
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryUniversity of VictoriaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersAlzheimer Society
KeywordsFace validityDementiaContent validityReliability (semiconductor)PsychologySet (abstract data type)Consistency (knowledge bases)Test (biology)Internal consistencyLong-term careClinical psychologyApplied psychologyMedicinePsychometricsComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Although family involvement is considered an important aspect of care for residents with dementia in long-term care facilities, measurement is lacking. The purpose of this study is to present a multi-item reliable measurement instrument assessing family perceived involvement. Literature reviews, observations within facilities, iterative consultations with an expert panel and extensive pilot testing of items for family perceived involvement were undertaken, to establish face and content validity. Two scales were developed: family perceived involvement and family assessment of importance of their involvement in individualized care for their resident relative. Strong evidence of face and content validity, internal consistency and test-retest reliability were established for both scales. Short versions of the original scales were derived via factor analysis. These instruments provide researchers and facilities with the ability to measure both degree of family perceived involvement and the importance the family places on that involvement using a relatively brief set of statements.

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)0.004
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.080
GPT teacher head0.378
Teacher spread0.297 · 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

Citations55
Published2007
Admission routes2
Has abstractyes

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