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Record W2332174066 · doi:10.1352/1934-9556-54.2.136

Evaluating the Implementation of Health Checks for Adults With Intellectual and Developmental Disabilities in Primary Care: The Importance of Organizational Context

2016· article· en· W2332174066 on OpenAlexaffabout
Janet Durbin, Avra Selick, Ian Casson, Laurie Green, Natasha A. Spassiani, Andrea Perry, Yona Lunsky

Bibliographic record

VenueIntellectual and developmental disabilities · 2016
Typearticle
Languageen
FieldMedicine
TopicHealth Promotion and Cardiovascular Prevention
Canadian institutionsSt. Michael's HospitalQueen's UniversityUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsOperationalizationContext (archaeology)Intellectual disabilityHealth carePsychologyProcess (computing)Qualitative researchNursingGerontologyApplied psychologyMedical educationMedicinePsychiatrySociologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Compared to other adults, those with intellectual and developmental disabilities have more health issues, yet are less likely to receive preventative care. One strategy that has shown success in increasing prevention activities and early detection of illness is the periodic comprehensive health assessment (the health check). Effectively moving evidence into practice is a complex process that often receives inadequate attention. This qualitative study evaluates the implementation of the health check at two primary-care clinics in Ontario, Canada, and the influence of the clinic context on implementation decisions. Each clinic implemented the same core components; however, due to contextual differences, some components were operationalized differently. Adapting to the setting context is important to ensuring successful and sustainable implementation.

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)0.029
metaresearch head score (Gemma)0.064
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 score0.373
Threshold uncertainty score0.741

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.064
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.001
Open science0.0020.003
Research integrity0.0010.001
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 head0.043
GPT teacher head0.335
Teacher spread0.292 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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