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Record W3110975428 · doi:10.1017/s0144686x20001671

Identification of determinants of healthy ageing in Italy: results from the national survey IDAGIT

2020· article· en· W3110975428 on OpenAlexaff
Erika Guastafierro, Ilaria Rocco, Barbara Corso, Nadia Minicuci, Fabio Vittadello, Alessandra Andreotti, Floriana Denitto, Valeria Crepaldi, Margherita Forgione, Matilde Leonardi, Davide Sattin

Bibliographic record

VenueAgeing and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsAlphora Research (Canada)
Fundersnot available
KeywordsAgeingGerontologyConfirmatory factor analysisPopulation ageingHealthy ageingComorbidityPopulationPerspective (graphical)Public healthMedicinePsychologyDemographyEnvironmental healthStructural equation modelingSociologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Healthy ageing is a public health problem globally. In Europe, the dependency ratio of the elderly is expected to increase by 21.6 per cent to 51.2 per cent in 2070. The World Health Organization (WHO) study on healthy ageing started in 2002 as a concept whereby all people of all ages should be able to live in a healthy, safe and socially inclusive way. The aim of this study is to present preliminary results of the project Identification of Determinants of Healthy Ageing in Italy (IDAGIT) that aimed to collect data on the active and healthy ageing of the Italian population aged over 18 using the conceptual framework of the WHO's ageing model. To link the determinants of the IDAGIT studies to those of the WHO model, we performed a confirmatory factor analysis which reported these variables as significant (in order of factor loading): smoking, cognition score, comorbidity, outdoor built environment, participation, working expertise and income. Considering comorbidity, 83.8 per cent of the sample declared not having any chronic diseases or to have only one, and regarding neurological diseases, only nine people had received a diagnosis of stroke. Regarding gender, the personal determinants and physical and social environments did not result in statistically significant differences, whereas we found statistical differences between the aged groups in all variables analysed. These results provide a first bio-psycho-social perspective on ageing in the Italian population.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.202
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.375
Teacher spread0.280 · 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.

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

Citations10
Published2020
Admission routes1
Has abstractyes

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