A 9-Step Theory- and Evidence-Based Postgraduate Medical Digital Education Development Model: Empirical Development and Validation
Bibliographic record
Abstract
BACKGROUND: Digital education tools (e-learning, technology-enhanced learning) can be defined as any educational intervention that is electronically mediated. Decveloping and applying such tools and interventions for postgraduate medical professionals who work and learn after graduation can be called postgraduate medical digital education (PGMDE), which is increasingly being used and evaluated. However, evaluation has focused mainly on reaching the learning goals and little on the design. Design models for digital education (instructional design models) help educators create a digital education curriculum, but none have been aimed at PGMDE. Studies show the need for efficient, motivating, useful, and satisfactory digital education. OBJECTIVE: Our objective was (1) to create an empirical instructional design model for PGMDE founded in evidence and theory, with postgraduate medical professionals who work and learn after graduation as the target audience, and (2) to compare our model with existing models used to evaluate and create PGMDE. METHODS: Previously we performed an integrative literature review, focus group discussions, and a Delphi procedure to determine which building blocks for such a model would be relevant according to experts and users. This resulted in 37 relevant items. We then used those 37 items and arranged them into chronological steps. After we created the initial 9-step plan, we compared these steps with other models reported in the literature. RESULTS: The final 9 steps were (1) describe who, why, what, (2) select educational strategies, (3) translate to the real world, (4) choose the technology, (5) complete the team, (6) plan the budget, (7) plan the timing and timeline, (8) implement the project, and (9) evaluate continuously. On comparing this 9-step model with other models, we found that no other was as complete, nor were any of the other models aimed at PGMDE. CONCLUSIONS: Our 9-step model is the first, to our knowledge, to be based on evidence and theory building blocks aimed at PGMDE. We have described a complete set of evidence-based steps, expanding a 3-domain model (motivate, learn, and apply) to an instructional design model that can help every educator in creating efficient, motivating, useful, and satisfactory PGMDE. Although certain steps are more robust and have a deeper theoretical background in current research (such as education), others (such as budget) have been barely touched upon and should be investigated more thoroughly in order that proper guidelines may also be provided for them.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".