DEVELOPMENT HISTORY AND THE CURRENT STATE OF PROFESSIONAL TRAINING IN HEALTH INFORMATICS IN CANADA
Bibliographic record
Abstract
The article studies the history and the current state of the professional training in health informatics in Canada. The specifics of healthcare informatization in Canada as a precondition for its formation are analyzed. At its initial stages, the computer technology was implemented into the provincial and territorial healthcare institutions slowly and unevenly. The computerization policy was decentralized, and this did not promote an effective medical information exchange. Thus, at the beginning of the 2000s, Canada set course for the centralized healthcare informatization. It required a qualified workforce and became a catalyst for the deve-lopment of professional training in health informatics in Canada. A retrospective analysis of the professional training in health informatics development in Canada is conducted. The research reveals that in its development professional training in health informatics has gone through the pre-institutional phase (the 1960s – 1980), which laid the basis for the appearance and further deve-lopment of professional training in health informatics, and the institutional phase (1981 – till present time), when it began to be implemented into the Canadian higher educational institutions. The characteristic features of the institutional phase include the rise of health informatics as an academic speciality; the conceptualization of professional training in health informatics; the rapid increase in the number of health informatics professional programs in the mid 2000s; the unification of methodological, scientific framework for training health informatics professionals. The current state of the professional training in health informatics in Canada is studied. It is concluded that the Canadian system of the health informatics professional training is built on the principles of degree education and lifelong learning. The educational process is organized in such a way that future health informatics professionals can receive a credential at different levels of the higher education, in particular a health informatics diploma or certificate in the non-degree granting institutions and Bachelor’s, Master’s and PhD degrees at universities. The analysis of the professional training content in health informatics enables to state that its development depends on the level of the higher education and is characterized by various combinations of academic disciplines in the health informatics curriculum within three knowledge domains – information sciences, health sciences, and management.
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
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".