The School of Health Information Science at the University of Victoria: towards an intergrative model for health informatics education and research.
Bibliographic record
Abstract
OBJECTIVES: There is an increasing need for well qualified health informatics practitioners and for educational programs that produce them. Since 1981, the School of Health Information Science at the University of Victoria has delivered a range of educational programs in health informatics. The School's objective has been to produce graduates who can assume a range of roles in health informatics, including managers, developers, researchers and evaluators of health care systems. METHODS: The approach taken by the School has been to provide an integrated 'holistic' approach to health informatics education that balances both theory and practice. The curriculum has emphasized interdisciplinary skills and has been based on a process of consultation with key stakeholders in both industry and academia. In addition, several new distance collaborative models for health informatics education (including a distributed MSc degree program) have been recently initiated through the University of Victoria with collaborating Canadian universities. RESULTS: To date, graduates of the programs offered have become highly sought after, with the demand for graduates of the programs continually exceeding the number of graduates. The core undergraduate curriculum has recently been undergone refinement to include training in new emerging areas of health informatics. In addition, a distributed MSc program has been successfully initiated by the School, currently with 23 students participating from dispersed geographical locations across Canada. CONCLUSIONS: The School of Health Information Science at the University of Victoria has been involved in providing unique interdisciplinary education in health informatics for over twenty years. The School continues to maintain its emphasis on integrated education, refining its curriculum and moving into new areas such as distance education and cross-Canadian collaborations.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".