A collaborative approach to health informatics capacity building - learning from Canadian practice in fostering research and education
Bibliographic record
Abstract
Objectives: Canada and Australia share two priority areas for HI workforce capacity building: to support research and education. This presentation will discuss a collaborative, nationwide health informatics (HI) PhD/Postdoc training program in Canada, which demonstrates a potential solution to meet these priorities. The status of HI higher education in Australia is briefly reviewed. The potential solution in resolving workforce shortage in Australia through adopting overseas practice is discussed, such as implementing a similar collaborative HI research training program across the country. Background: A Think Tank organized by Commonwealth Department of Health and Ageing in 2003 explored the issue of HI workforce capacity building and identified a list of priorities for action. These included support for research and education. One proposed strategy for addressing research support was to: “Fund the formation of a virtual collective of researchers.” Proposed strategies for enhancing education included: “exploring the potential of collaboration between universities for efficiency gains.” This paper discusses how to move the agenda forward based on case study of a similar Canadian initiative for addressing similar issues. Methods: A comparative case study approach was taken that integrated a literature research, which identified the importance of support for research and education in HI workforce capacity building in Australia, document review, interview of key informants and observation of project progress. Results and Implications: In response to the national need for solutions to HI research and education capacity building, this paper explored the possibility of introducing a program similar to a nationally funded Canadian project with eight universities that established a collaborative HI PhD/Postdoc research training program. However, the education system in Australia is different from that of Canada. Substantial strategic and tactical issues remain to be solved before such an initiative could move forward. Despite the challenges, Canadians have demonstrated that such an initiative can succeed and play a vital role in enhancing HI research and education capacity nationwide.
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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 | 0.077 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.037 | 0.037 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.007 | 0.038 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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, 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".