Reflections on an Arranged Marriage between Bioinformatics and Health Informatics
Bibliographic record
Abstract
OBJECTIVE: To compare the discussions of two workshops held during 2001 by two Canadian organisations, HEALNet, a Network of Centres of Excellence for research in health information applications, and Genome Canada, a national research funding agency for genomics and proteomics, in collaboration with the Institute of Genetics of the Canadian Institutes of Health Research, to examine strategic research development in Health Informatics and Bioinformatics respectively. METHODS: Invited workshops with structured debate. Concept analysis of preparative material and debates. RESULTS: A predominantly common set of concepts was discerned from both workshops. Analysis of published definitions showed an inability to distinguish a definition that would suggest that health informatics and bioinformatics are separate disciplines. In both workshops there was evidence of deep concerns of identity, the lack of clear structures to support research funding as well as uncertainty in distinguishing between service and research. CONCLUSIONS: Many deep issues currently inhibit the recognition and funding of research in health and bioinformatics in Canada and elsewhere. Some of these issues are common to both health and bioinformatics. The overlap in prevailing definitions, research concerns and methodological content in the respective domains suggest that common research needs should be better identified and reinforced for the benefit of both.
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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 | 0.097 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.035 | 0.046 |
| Scholarly communication | 0.026 | 0.013 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.019 | 0.036 |
| 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".