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Record W160421349

North American Consortium on Rehabilitation Engineering and Technology for the Individual (NARETI)

2013· article· en· W160421349 on OpenAlexaboutno aff
Michelle Silverthorn, Karla Bustamante

Bibliographic record

Venuee-Publications@Marquette (Marquette University) · 2013
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careMedical educationHealth technologyHealthcare deliveryFocus groupRelevance (law)Public relationsPolitical sciencePsychologyBusinessMedicineMarketing
DOInot available

Abstract

fetched live from OpenAlex

The availability and accessibility of appropriate rehabilitative healthcare, medical technology and treatment is an important national and international issue of particular relevance due to recent national healthcare reform initiatives. The focus of this project was to increase global competencies and awareness among biomedical engineers of the differing rehabilitative healthcare needs in North America via student exchange with consortium institutions in the U.S., Canada and Mexico. The aim was to increase understanding of alternative healthcare delivery systems with respect to technology and interaction with diverse client populations in a clinical setting and to enhance the development and technology transfer of new scientific tools and techniques, medical devices, and related biomedical research. To date, more than 50 undergraduates have expressed interest in these programs, with over 30 students completing applications, and travel awards extended to 18 students (16 of whom opted to participate in study abroad experiences). Assessment tools included: a healthcare survey, two case study reports, global perspectives inventory documenting cultural differences, cultural comforts and the campus environment for culture and cultural tolerance, and interviews of the exchange participants and faculty research mentors by the external program evaluator.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 teacher head, 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

Citations0
Published2013
Admission routes1
Has abstractyes

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