Utility of science, technology and innovation governance for occupational discourses from the perspective of occupational therapy students
Bibliographic record
Abstract
BACKGROUND: Science, technology and innovation (STI) governance concerns itself with the societal impact of STI. Occupation, whether used with the meaning of paid, unpaid work or any activity that is considered meaningful to the individual on an everyday basis, is one area of societal impact of STI. Fields such as occupational therapy, occupational science and occupational health and safety concern themselves with the relationship between occupation and the health and well-being of human beings albeit all with different foci. OBJECTIVE: To ascertain the knowledge of students from two Occupational Therapy programs on STI governance, specific STI products and their views on the impact of STI governance and STI products on occupational therapy and its clients. METHODS: Online survey employing Yes/No' questions with comment boxes and open-ended textbox questions. Descriptive quantitative and thematic qualitative data was generated. RESULTS: Students were unfamiliar with STI governance discourses but felt that they should be aware of them. Students stated that how one governs STI impacts occupational therapy on all levels and that the occupational therapy community has expertise that would enrich STI governance discourses around occupation. CONCLUSION: Education actions seem to be warranted on the level of students and practitioners by the occupational therapy and STI governance communities.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".