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Record W3107556935 · doi:10.1111/jar.12833

Changes over time in the management of long‐term conditions in primary health care for adults with intellectual disabilities, and the healthcare inequality gap

2020· article· en· W3107556935 on OpenAlexaff
Laura Anne Hughes-McCormack, Nicola Greenlaw, Paula McSkimming, Colin McCowan, Kevin Ross, Linda Allan, Angela Henderson, Craig Melville, Jill Morrison, Sally‐Ann Cooper

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2020
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsInstitute of Infection and Immunity
FundersScottish Government
KeywordsInequalityHealth carePopulationMedicinePrimary careIntellectual disabilityPrimary health careGerontologyDemographyFamily medicineEnvironmental healthPsychiatryMathematicsEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Quality of primary healthcare impacts on health outcomes. This study aimed to quantify trends in good practice and the healthcare inequalities gap. METHOD: Indicators of best-practice management of long-term conditions and health promotion were extracted from primary healthcare records on 721 adults with intellectual disabilities in 2007-2010, and 3638 in 2014. They were compared over time, and with the general population in 2014, using Fisher's Exact test and ordinal regression. RESULTS: Management improved for adults with intellectual disabilities over time (OR = 5.32; CI = 2.69-10.55), but not for the general population (OR = 0.74; CI = 0.34-1.64). However, it remained poorer, but to a lesser extent, compared with the general population (OR = 0.38; CI = 0.20-0.73 in 2014, and OR = 0.05; CI = 0.02-0.12 in 2007-2010). In 2014, health care was comparable to the general population on 49/78 (62.8%) indicators. CONCLUSIONS: The extent of the healthcare inequality gap reduced over this period, but remaining inequalities highlight that further action is still necessary.

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)0.006
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.114
GPT teacher head0.397
Teacher spread0.283 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

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