Perspectives on the implementation of health informatics curricula frameworks
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic highlighted the necessity of equipping health professionals with knowledge and skills to effectively use digital technology for healthcare delivery. However, questions persist about the best approach to effectively educate future health professionals for this. A workshop at the 15th Nursing Informatics International Congress explored this issue. OBJECTIVE: To report findings from an international participatory workshop exploring pre-registration informatics implementation experiences. METHODS: A virtual workshop was held using whole and small group interactive methods aiming to 1) showcase international examples of incorporating health informatics into pre-registration education; 2) highlight essential elements and considerations for integrating health informatics into curricula; 3) identify integration models of health informatics; 4) identify core learning objectives, resources, and faculty capabilities for teaching informatics; and 5) propose curriculum evaluation strategies. The facilitators' recorded data and written notes were content analysed. RESULTS: Fourteen participants represented seven countries and a range of educational experiences. Four themes emerged: 1) Design: scaffolding digital health and technology capabilities; 2) Development: interprofessional experience of and engagement with digital health technology capabilities; 3) implementation strategies; and 4) Evaluation: multifaceted, multi-stakeholder evaluation of curricula. These themes were used to propose an implementation framework. DISCUSSION: Workshop findings emphasise global challenges in integrating health informatics into curricula. While course development approaches may appear linear, the learner-centred implementation framework based on workshop findings, advocates for a more cyclical approach. Iterative evaluation involving stakeholders, such as health services, will ensure that health professional education is progressive and innovative. CONCLUSIONS: The proposed implementation framework serves as a roadmap for successful health informatics implementation into health professional curricula. Prioritising engagement with health services and digital health industry is essential to ensure the relevance of implemented informatics curricula for the future workforce, acknowledging the variability in placement experiences and their influence on informatics exposure, experience, and learning.
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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 | 0.123 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".