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Vieillir en santé à Genève : l’approche novatrice du projet VieSA

2023· article· fr· W4388141670 on OpenAlexaff
Samuel Périvier, Noémie De Bom, Eleni‐Marina Ashikali, Irina Ioniță, Laura Mastromauro, Aude Tholomier, Catherine Ludwig, Catherine Busnel, Christophe Graf

Bibliographic record

VenueRevue Médicale Suisse · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicAging, Elder Care, and Social Issues
Canadian institutionsInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsHumanitiesPopulation ageingPolitical scienceHealthy ageingGerontologyPopulationMedicineArtAgeingEnvironmental health

Abstract

fetched live from OpenAlex

With Switzerland's population ageing, promoting healthy ageing remains a public health issue. This represents a challenge for the healthcare system, which is still mainly focused on curative or palliative care. It has been clearly established that it is possible to maintain the functional capacity of older people by taking early action on health-related behaviors. The VieSA (Vieillissement en Santé) project in the canton of Geneva, inspired by the WHO's ICOPE programme, offers innovative ways of promoting healthy ageing for and by seniors, by focusing on maintaining seniors' resources rather than targeting any deficits.

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)0.071
metaresearch head score (Gemma)0.066
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: none
Teacher disagreement score0.071
Threshold uncertainty score0.376

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.066
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0030.002
Scholarly communication0.0090.004
Open science0.0040.009
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0220.005

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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