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Canadian Healthcare Perspective in Traumatic Brain Injury Rehabilitation

2007· review· en· W2000735095 on OpenAlexaffabout
Nora Cullen

Bibliographic record

VenueJournal of Head Trauma Rehabilitation · 2007
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsRehabilitationPerspective (graphical)Health careWork (physics)Traumatic brain injuryQuality (philosophy)NursingMedicineMedical educationPsychologyMedical emergencyPhysical therapyPolitical sciencePsychiatryComputer science

Abstract

fetched live from OpenAlex

Brain injury rehabilitation is based on principles of universal access to healthcare in a publicly funded system and is available to all residents in Canada. However, a variability of resources exists because of the vastness of the country and differences in provincial funding. Rehabilitation is typically provided by an interprofessional team and is client centered, goal directed, evidence based, and aimed at improving quality of life for survivors of brain injury. Work is being done exploring different models of care including community treatment and the use of rehabilitation therapists. Patient education is an ongoing priority. Data collection is considered mandatory in many centers for program evaluation and quality improvement. There is a clear need for coordinated standards of care across the country.

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)0.006
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: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.684

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0070.014
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0170.002

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 head0.133
GPT teacher head0.486
Teacher spread0.353 · 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
GenreReview

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

Citations26
Published2007
Admission routes2
Has abstractyes

Explore more

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