Application of PBL Combined with Multimedia Technology in Standardized Training of Ophthalmic Residents
Bibliographic record
Abstract
To study the application of PBL combined with multimedia technology in standardized training of residents. Retrospective analysis was carried out to select 15 general scientists in the rotation of ophthalmology in the standardized training of 2020 residents in the Second Clinical Medical College of Inner Mongolia University for Nationalities from May 2022 to May 2023, and 15 general scientists in the rotation of 2021 residents in the standardization training of ophthalmology. They were divided into the teaching method group of PBL combined with multimedia technology (observation group, n=15). The traditional teaching method group (control group, n=15) was used to statistically analyze the assessment results of residents in the two groups and their satisfaction with teaching methods. The theoretical, clinical skills and comprehensive ability of residents in the observation group were significantly higher than those in the control group (P<0.05).The residents in the observation group were significantly higher than those in the control group (80.0% (12/15), 73.3% (11/15), 73.3% (11/15), 73.3% (14/15), 80.0% (12/15), 80.0% (12/15)86.7% (13/15), the difference was statistically significant (P<0.05).It is concluded that PBL standardized training combined with multimedia technology teaching method is more effective than the traditional single teaching method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".