North American Consortium on Rehabilitation Engineering and Technology for the Individual (NARETI)
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".