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Record W4403880329 · doi:10.2147/jmdh.s437850

An Innovative Preventive and Rehabilitative Model for Acute Care: The Independence Model

2024· article· en· W4403880329 on OpenAlexafffund
E.N. Naranjo, I Pillay, Sandra J Squire, Agnes Black, M. John Gill

Bibliographic record

VenueJournal of Multidisciplinary Healthcare · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsVancouver General HospitalUniversity of British Columbia HospitalUniversity of British ColumbiaProvidence Health Care
FundersProvidence Health Care
KeywordsIndependence (probability theory)MedicineComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

Purpose: The Independence Model (TIM) is a new rehabilitative model of care implemented in an acute care hospital to address patients' functional decline and a high vacancy rate for rehabilitation therapists. Methods: TIM was developed by a team with expertise in evidence, scope of practice and roles, population care needs, and current state related to rehabilitation. TIM utilizes rehabilitation assistants, supervised by physical therapists, occupational therapists or speech-language pathologists, to assist patients in functional areas such as ambulation, activities of daily living (ADLs), cognition and communication. The planning team ensured patient engagement, utilized change management principles, and evaluated the effectiveness of care. Results: Preliminary evaluation of TIM was positive, with staff reporting improved caseload quality and patients feeling more prepared for discharge. Conclusion: This study suggests innovative models of care, such as TIM, can help address the functional needs of patients while navigating the global health human resource crisis.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.109
GPT teacher head0.555
Teacher spread0.446 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes2
Has abstractyes

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