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Record W3107018345 · doi:10.1177/0840470420974040

An exploration of Canadian transitional care programs for older adults

2020· article· en· W3107018345 on OpenAlexafffundabout
Lori E. Weeks, Brittany Barber, Eileigh Storey MacDougall, Marilyn Macdonald, Ruth Martin‐Misener, Grace Warner

Bibliographic record

VenueHealthcare Management Forum · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsDalhousie University
FundersDalhousie University
KeywordsTransitional careScope (computer science)Health careNursingMedicineGerontologyPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Transitional care programs are effective for improving patient outcomes upon discharge from acute care services and reducing the burden of healthcare costs; however, little is known about the types of transitional care programs for older adults across Canada. This exploratory study gathered an in-depth understanding of Canadian transitional care programs and described how each program functions to support older adults and family/friend caregivers. Nine key informants were interviewed about the development of transitional care programs within four Canadian provincial regions including Atlantic, Central, Prairie, and West Coast. Key facilitators and barriers influencing the development and long-term success of transitional care programs included program scope, program structure, continuity of care, funding, and health system infrastructure. Future research is required to identify how a broad range of transitional care programs operate and to disseminate knowledge with health leaders and decision-makers to ensure transitional care programs are embedded as essential health system services.

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)0.004
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.929

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0180.002
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.377
Teacher spread0.296 · 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

Citations20
Published2020
Admission routes3
Has abstractyes

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