Telessaúde e Informática em Saúde. A viagem rumo à convergência COACH: Canada’s Health Informatics Association e a Canadian Society of Telehealth Merge
Bibliographic record
Abstract
Canada has a long and strong history with health informatics and telehealth. With associations supporting each since 1976 for health informatics and 1998 for telehealth, the work of automation in health care has been well served by committed and capable professionals. Health informatics and telehealth must be seamlessly integrated to provide optimum service to care providers and their patients yet two worlds of telehealth and health informatics have grown up in silos. The respective implementation projects have been launched and delivered by separate departments or staff, the technologies have grown up on separate pathways, the funding has often come from distinct and different sources, the education and training has been delivered by different university departments, and the associations supporting the members and industry grew up separately. Recognizing that working together would begin to bridge the silos, the Canadian Society of Telehealth (CST) and Canada's Health Informatics Association (COACH) embarked on a journey to show leadership in integrating the two worlds, initially by through joint projects, and ultimately by merging the two organizations. This paper describes the rationale, process, benefits and lessons learned in creating a single association to serve the Canadian Telehealth and Health Informatics 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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".