Artificial intelligence and machine learning
Bibliographic record
Abstract
Artificial Intelligence/Machine Learning (AI/ML) increasingly influences products and processes used by social workers, occupational therapists, audiologists, nurses and speech language pathologists (health professionals for short) in general and in their rehabilitation practice. Health professionals are expected to fulfill many roles and within the narrative of AI/ML health professionals can hold multiple roles. We performed a scoping review using the academic database Scopus, the 70 databases accessible through EBSCO-Host and the database Canadian Newsstream through which we accessed 300 Canadian English language papers as sources. We found minimal engagement with the roles of the covered health professionals related to AI/ML whereby nurses were covered much more than the other health professionals. The main role mentioned for all occupations covered in our study was the one of clinical user. Many other roles expected from health professionals such as being advocates for their field and clients or being policy developers, educators and researchers were rarely or not at all mentioned depending on the health professional. Our role narrative analysis of AI/ML related to the covered health professionals reveals significant gaps in need to be filled.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
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".