Bibliographic record
Abstract
Background. While occupational therapy’s inception was from the Arts and Crafts movement and the moral treatment movement with war veterans, the profession has evolved to requiring a professional entry-level master’s degree to practice, and involves complex relationships with clients across the life span. Throughout history, a consistent impact of each industrial revolution has been the loss of jobs to automation. This consequence is even more profound today with the exponential growth of innovations and automation. Purpose. The objectives of this article are to (a) set the context by reviewing the evolution, or five eras, of occupational therapy in Canada; (b) present what is meant by the “Fourth Industrial Revolution”; and (c) examine the technological innovations faced by occupational therapists and our clients as we enter the “sixth” era of occupational therapy in Canada. Key Issues. Although occupational therapy, as a profession, has low risk for automation, a great number of our clients will not be able to reskill fast enough to keep up with job market requirements. Telerehabilitation, the Internet of Things, virtual reality, 3-D printing, robotics, artificial intelligence, and autonomous vehicles are challenging ways occupational therapists provide services to clients. Implications. It is recommended that occupational therapists engage with disciplines beyond current typical connections, as our expertise is called upon to advocate for ourselves and our clients who are end users of these technologies.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".