Mapping eHealth Education: Review of eHealth Content in Health and Medical Degrees at a Metropolitan Tertiary Institute in Australia
Bibliographic record
Abstract
BACKGROUND: With the increasing use of digital technology in society, there is a greater need for health professionals to engage in eHealth-enabled clinical practice. For this, higher education institutions need to suitably prepare graduates of health professional degrees with the capabilities required to practice in eHealth contexts. OBJECTIVE: This study aims to understand how eHealth is taught at a major Australian university and the challenges and suggestions for integrating eHealth into allied health, nursing, and medical university curricula. METHODS: Cross-disciplinary subject unit outlines (N=77) were reviewed for eHealth-related content, and interviews and focus groups were conducted with the corresponding subject unit coordinators (n=26). Content analysis was used to identify themes around challenges and opportunities for embedding eHealth in teaching. RESULTS: There was no evidence of a standardized approach to eHealth teaching across any of the health degrees at the university. Where eHealth content existed, it tended to focus on clinical applications rather than systems and policies, data analysis and knowledge creation, or system and technology implementation. Despite identifying numerous challenges to embedding eHealth in their subjects, unit coordinators expressed enthusiasm for eHealth teaching and were keen to adjust content and learning activities. CONCLUSIONS: Explicit strategies are required to address how eHealth capabilities can be embedded across clinical health degrees. Unit coordinators require support, including access to relevant information, teaching resources, and curriculum mapping, which clearly articulates eHealth capabilities for students across their degrees. Degree-wide conversations and collaboration are required between professional bodes, clinical practice, and universities to overcome the practical and perceived challenges of integrating eHealth in health curricula.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".