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Record W1978656758 · doi:10.1016/j.hcmf.2011.12.002

Enhanced Activation and Restorative Care

2012· article· en· W1978656758 on OpenAlexaffabout
Heather Crawford, Sherry Anderson, Robin TeKamp, Vaughnette Chatzikiriakos, Deanne Osborne

Bibliographic record

VenueHealthcare Management Forum · 2012
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutions3M (Canada)
Fundersnot available
KeywordsDeconditioningMedicineGerontologyNursingPhysical therapy

Abstract

fetched live from OpenAlex

As the Canadian population ages, it becomes increasingly important for hospitals to address and implement programs and policies that ensure elderly patients are receiving the right care in the right place at the right time. Despite the vast majority of elderly patients being healthier than ever, it is important to recognize the rapid deconditioning that occurs when they are admitted to an acute care hospital. Although it is recognized that children need to be treated differently, we fail to recognize the same is true for older adults. The Enhanced Activation Program was conceptualized during consultation with community partners while discussing the aging at home strategy. It was during these consultations that gaps in service for the frail elderly were identified and the Enhanced Activation Program was developed.

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)0.002
metaresearch head score (Gemma)0.003
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: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.024
GPT teacher head0.321
Teacher spread0.297 · 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
GenreOther

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

Citations5
Published2012
Admission routes2
Has abstractyes

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