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Record W4388852086 · doi:10.1111/jar.13169

Towards developing an intervention to support periodic health checks for adults with intellectual and developmental disabilities: Striving for health equity

2023· article· en· W4388852086 on OpenAlexaffabout
Karen McNeil, Jillian Achenbach, Beverley Lawson, Alannah Delahunty‐Pike, Brittany Barber, Heidi Diepstra

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2023
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsSurrey Place CentreDalhousie University
Fundersnot available
KeywordsIntellectual disabilityStakeholderIntervention (counseling)Equity (law)Health carePsychologyPrimary careNursingMedicineMedical educationPublic relationsFamily medicinePolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Although the Canadian Consensus Guidelines for Primary Care of Adults with Intellectual and Developmental Disabilities recommends conducting periodic health checks in primary care, uptake is lacking. This study seeks to understand factors influencing the conduct of periodic health checks and identify what needs to change to increase them. METHOD: Qualitative data from five stakeholder groups (adults with intellectual and developmental disabilities, primary care providers, administrative staff, family, disability support workers) was guided by the Behaviour Change Wheel and the Theoretical Domains Framework to identify barriers and 'what needs to change' to support periodic health checks. RESULTS: Stakeholders (n = 41) voiced multiple barriers. A total of 31 common and 2 unique themes were identified plus 33 items 'needing to change'. CONCLUSION: Despite barriers, stakeholders saw merit in periodic health checks as a preventative and equitable healthcare offering for adults with intellectual and developmental disabilities. Results will inform future intervention development steps.

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)0.012
metaresearch head score (Gemma)0.045
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.045
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.248
GPT teacher head0.490
Teacher spread0.242 · 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 designQualitative
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
Published2023
Admission routes2
Has abstractyes

Explore more

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