Towards an epistemological understanding of healthcare informatics: Academic backgrounds of the faculty
Bibliographic record
Abstract
Healthcare informatics is a relatively new field to academia and is multi-disciplinary by nature. Although the field of health informatics encompasses several disciplines and subject areas that are familiar and long standing, the field itself is still in a formative state that allows many disciplines to contribute to the field through teaching and curriculum development in a way that may not be possible in more established educational programs. The purpose of this pilot study was to start to define the cross-disciplinary nature of a Healthcare Informatics faculty. Researchers in the field agree that the discipline includes a full spectrum of courses, but the diversity of faculty backgrounds remains vague. In this pilot study, one trend was apparent in the academic backgrounds of the Healthcare Informatics faculty; Computer Science was the most common academic background of the faculty (10 PhD’s, 8 Graduate and 6 Undergraduate Degrees). Interestingly, four faculty members earned a PhD in Health Informatics and no faculty member had earned a graduate/undergraduate degree in Healthcare informatics. The faculty members of the ten universities investigated in this pilot study indicated 45 unique Doctoral disciplines. By any measure, that would be considered inter-disciplinary.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".