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Record W2617676879 · doi:10.1017/s0714980817000186

How’s Your Health at Home: Frail Homebound Patients Reported Health Experience and Outcomes

2017· article· fr· W2617676879 on OpenAlexaffabout
Margaret J. McGregor, Jay Slater, John P. Sloan, Kimberlyn McGrail, Anne Martin-Matthews, Shannon Berg, Alyson Plecash, Leila Sloss, Johanna Trimble, Janice M. Murphy

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2017
Typearticle
Languagefr
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsMedicineGerontologyOddsOdds ratioQuarter (Canadian coin)Home healthFamily medicineHealth careLogistic regression

Abstract

fetched live from OpenAlex

RÉSUMÉ Pour notre sondage, nous avons utilisé une méthodologie mixte basée sur le Web (How’s Your Health – Frail) pour examiner la santé des adultes fragiles (78% âgés de 80 ans et plus) inscrits à un programme de soins primaires à domicile à Vancouver, au Canada. Soixante pour cent des répondants admissibles ont participé, représentant plus d’un quart (92/350, 26,2%) de tous les individus qui reçoivent le service. Malgré des niveaux élevés de co-morbidité et de dépendance fonctionnelle, 50% ont jugé leur santé aussi bonne, très bonne ou excellente. Les ratios de cotes ajustés pour l’auto-évaluation de sa santé positive étaient de 7,50, 95 pour cent d’intervalle de confiance (IC) [1,09, 51,81] et 4,85, 95% CI [1,02, 22,95] pour l’absence de symptômes gênants et le pouvoir de parler à la famille ou amis, respectivement. Des réponses narratives aux questions sur la fin de vie et la vie avec une maladie sont également décrites. Les résultats suggèrent que l’accent mis sur la gestion des symptômes, et le soutien des contacts sociaux, peut améliorer la santé des personnes âgées fragiles.

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)0.005
metaresearch head score (Gemma)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.287
Teacher spread0.257 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicFrailty in Older AdultsFrench-language works237,207