North American Medical Informatics (NAMI)
Bibliographic record
Abstract
Founded in 1975, COACH: Canada's Health Informatics Association is the organization focused on advancing health informatics (HI) practices and professionalism in Canada.Representing more than 1,300 members, COACH is committed to a vision of taking health informatics mainstream.The association's mandate is to promote the understanding and adoption of HI within the Canadian health system through leadership, professional development, advocacy and a strong, diverse membership.COACH focuses on fulfilling this mandate through a number of key initiatives in addition to the continued enhancement of member services in the areas of networking, information and knowledge sharing, conferences, education, alliances and thought leadership and advocacy.Completing its 32nd year as the national association for HI, COACH continues to develop significant and exciting initiatives to provide leadership in the evolution of HI as a profession in Canada.COACH continues, in conjunction with the Canadian Institute for Health Information (CIHI), to host the largest annual e-Health conference in Canada.COACH and the Canadian Healthcare Information Technology Trade Association (CHITTA) also co-host the annual Canadian Health Informatics Awards program that recognizes achievment and contribution in the HI community through a growing number of personal, project and company-based awards.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.143 | 0.051 |
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".