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Record W4362523603 · doi:10.1093/ije/dyad026

Cohort Profile: The Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA)

2023· article· en· W4362523603 on OpenAlexfundno aff
Charlotte E. Neville, Frances Burns, Sharon Cruise, Angela Scott, Dermot O’Reilly, Frank Kee, I.G. Young

Bibliographic record

VenueInternational Journal of Epidemiology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
FundersEconomic and Social Research CouncilWolfson FoundationQueen's University BelfastOffice of the First Minister and Deputy First MinisterQueen's UniversityWellcome Trust
KeywordsCohortAgeingCohort studyMedicineCohort effectGerontologyDemographyInternal medicine

Abstract

fetched live from OpenAlex

<b>Key Features</b><br/><br/>• The Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) is Northern Ireland’s largest health and social care cohort collecting longitudinal data from a representative sample of the over-50s population.<br/><br/>•8283 participants aged ≥50 years and living in private residential accommodation were recruited, from a randomized, stratified sample of Northern Ireland addresses, to the Wave 1 cohort between December 2013 and March 2016. A follow-up (Wave 2) of the cohort took place between May 2017 and November 2019, with a response rate of 73% (n = 6152).<br/><br/>•NICOLA participants will undertake a detailed survey, in their own home, every 2 years with a detailed health assessment every 4 years.<br/><br/>•The NICOLA data set comprises a diverse range of objective and subjective measures of physical and mental health, life expectancy, disability, education, economic activity, social participation and support, household and family structures; and uniquely to NICOLA, the effects of ‘the Troubles’, a detailed ophthalmological assessment and a dietary assessment. Inclusion of unique measures allows analysis of the social and contextual influences on the distribution of health status determinants particular to Northern Ireland.<br/><br/>•The collection of data via subjective reporting in interviews and self-completion methodologies combined with objective measurement of health and wellbeing allows detailed analysis of the ageing profiles.<br/><br/>•The NICOLA cohort is flagged on the National Health Applications Infrastructure Service to enable robust and comprehensive linkage for identification of participant outcomes and affecting factors.<br/><br/>•The harmonization of health status measures with other ageing cohorts allows cross cohort comparative analysis, those of particular relevance being the English Longitudinal Study of Ageing (England) and The Irish Longitudinal Study of Ageing (Republic of Ireland). Comparability of NICOLA to other ageing studies will also be enabled via the Gateway to Global Aging Data (G2G) and with NICOLA data being archived within the Dementias Platform UK (https://www.dementiasplatform.uk/research-hub/data-portal).<br/><br/>•Access to the anonymized data is by direct application to the NICOLA study (https://nicola.qub.ac.uk/sites/NICOLA/InformationforResearchers/#requesting-access-to-nicola-data-or-biological-samples-910951-1).<br/><br/><br/>

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)0.014
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.198
GPT teacher head0.537
Teacher spread0.339 · 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

Citations26
Published2023
Admission routes1
Has abstractyes

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