The Australian Health Informatics Competencies Framework and Its Role in the Certified Health Informatician Australasia (CHIA) Program
Bibliographic record
Abstract
The Certified Health Informatician Australasia (CHIA) program consists of an online exam, which aims to test whether a candidate has the knowledge and skills that are identified in the competencies framework to perform as a health informatics professional. The CHIA Health Informatics Competencies Framework provides the context in which the questions for the exam have been developed. The core competencies for health informatics that are tested in the exam have been developed with reference to similar programs by the American Medical Informatics Association, the International Medical Informatics Association and COACH, Canada's Health Informatics Association, and builds on the previous work done by the Australian Health Informatics Education Council. This paper shows how the development of this competency framework is helping to raise the profile of health informaticians in Australasia, contributing to a wider recognition of the profession, and defining more clearly the body of knowledge underpinning this discipline. This framework can also be used as a set of guidelines for recruiting purposes, definitions of career pathways, or the design of educational and training activities. We discuss here the current status of the program, its resultsandprospectsfor the future.
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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".