Creating a Pilot Educational Psychiatry Website: Opportunities, Barriers, and Next Steps
Bibliographic record
Abstract
BACKGROUND: While medical students and residents may be utilizing websites as online learning resources, medical trainees and educators now have the opportunity to create such educational websites and digital tools on their own. However, the process and theory of building educational websites for medical education have not yet been fully explored. OBJECTIVE: To understand the opportunities, barriers, and process of creating a novel medical educational website. METHODS: We created a pilot psychiatric educational website to better understand the options, opportunities, challenges, and processes involved in the creation of a psychiatric educational website. We sought to integrate visual and interactive Web design elements to underscore the potential of such Web technology. RESULTS: A pilot website (PsychOnCall) was created to demonstrate the potential of Web technology in medical and psychiatric education. CONCLUSIONS: Creating an educational website is now technically easier than ever before, and the primary challenge no longer is technology but rather the creation, validation, and maintenance of information for such websites as well as translating text-based didactics into visual and interactive tools. Medical educators can influence the design and implementation of online educational resources through creating their own websites and engaging medical students and residents in the process.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".