General practitioners’ knowledge gaps regarding age related macular degeneration and effectiveness of their e-learning training
Bibliographic record
Abstract
Objective: To assess the General Practitioners (GPs) knowledge regarding Age Related Macular Degeneration (ARMD) in order to better design a specific training program, then to evaluate the achievement of their clinical competences in the care of ARMD after taking a specific training program. Methods: The first part of the study was a postal questionnaire survey sent to all GPs in Castilla & León. An interactive e-learning program with virtual tutorials was specifically designed for them to cover the identified gaps. Results: 725 GPs out of 2365 answered the survey, and 44% of them recognized they did not know the best screening test (Amsler grid). Around 30% did not know about the bilateralism, prevalence, risk factors, prevention or treatment of ARMD. Younger physicians knew more about ARMD. A specifically designed course was offered to 205 GPs and 164 completed it. We found a higher number of female and younger doctors doing the course compared with the group answering the previous survey. The answer “I do not know” almost disappeared after the course. The probability of suspecting a typical case of ARMD was higher after the course. Conclusions: The specific interactive online training program focused on covering the identified GPs´ gaps led to an effective 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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".