MétaCan
Menu
Back to cohort
Record W2889002071 · doi:10.1017/s0714980818000272

Nursing Homes Without Walls for Aging in Place

2018· article· fr· W2889002071 on OpenAlexaffabout
Suzanne Dupuis‐Blanchard, Odette N. Gould

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2018
Typearticle
Languagefr
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMount Allison UniversityUniversité de Moncton
Fundersnot available
KeywordsAging in placeAssisted livingNursing homesIndependence (probability theory)NursingGerontologyPopulation ageingAssisted Living FacilityFace (sociological concept)PopulationMedicineSociologyEnvironmental health

Abstract

fetched live from OpenAlex

RÉSUMÉ Étant donné l’urgence de trouver des solutions innovantes et efficientes pour la prestation de services communautaires favorisant le «vieillir chez soi», il est nécessaire d’identifier de nouvelles solutions mettant à profit les infrastructures existantes. Dans la présente étude séquentielle à méthode mixte, nous avons exploré le rôle que les centres d’hébergement pour personnes âgées pourraient jouer dans l’offre de services destinés à une population cible non traditionnelle, soit les aînés avec pertes d’autonomie vivant dans la communauté. Quarante-deux (n=42) centres d’hébergement pour personnes âgées du Nouveau-Brunswick ont complété un sondage en ligne et 10 de ces établissements ont accepté d’accorder des entretiens. Les résultats montrent que 100 % des participants sont d’avis que les centres d’hébergement pourraient offrir des services aux personnes âgées dans la communauté afin de favoriser le vieillir chez soi. Les résultats suggèrent que les centres d’hébergement peuvent apporter des solutions efficientes et innovantes en ce sens.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.001

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 head0.023
GPT teacher head0.311
Teacher spread0.288 · 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 designNot applicable
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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicGeriatric Care and Nursing HomesFrench-language works237,207