ПЕРІОДИЗАЦІЯ РОЗВИТКУ ПРОФЕСІЙНОЇ ОСВІТИ ФАХІВЦІВ З МЕДИЧНОЇ ІНФОРМАТИКИ У КАНАДІ
Bibliographic record
Abstract
The article provides a retrospective review of the development of health informatics professional education in Canada. The authors propose to divide its history into six periods using chronological and problem-based approach. The first period is characterized as a preparatory one as it laid the foundations of the emergence and further development of health informatics professional education in Canada. The second period started in 1981 and may be defined as the time of the birth of health informatics as an academic specialty. It is related to the establishment of the first Canadian health informatics department and the introduction of a Bachelor’s degree programme in health informatics at the University of Victoria. The characteristic feature of the third period is developing model curricula as well as educational conceptions, which gave a distinct differentiation of trajectories of health informatics education evolution. They are acquiring health informatics competency by medical students and practitioners; training health informatics professionals, and introducing Masters’ and Doctor’s degree programmes to train health informatics scientists. Regarding the fourth period, health informatics professional education in Canada experienced flourishing that started in the 2000s under the influence of Canada’s centralized policy on informatization of its health care system. The start of the fifth period is connected with unification of the scientific and methodological framework for training health informatics professionals in Canada. Since the beginning of the 21st century, health informatics professional organizations, academia, and employees have been engaged in defining health informatics professional core competencies and career matrix. Finally, in the modern period, the key task is to develop effective mechanisms for ensuring quality of the Canadian health informatics professional education.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Theoretical or conceptual | high |
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".