Effects of teaching in actual scene on neuropsychology by consistency check
Bibliographic record
Abstract
Objective To explore the effect of consistency check combined with teaching in actual scene on clinical teaching practice of neuropsychology. Methods From June to August in 2009, 100 medical staff from 21 military hospitals were trained for the assessment methods of Peking Union Medical College Hospital [PUMCH] version of Montreal Cognitive Assessment(Mo CA-P) by traditional lecture, illustration and practice in Beijing for 3 times. During training period, teaching in actual scene combined with consistency check was performed. The median and the standard score of these staff were tested by consistency check, and the problems found by consistency check would be solved during the second training program. Results The results of consistency check after the first training found that the median and the standard scores of medical staff showed significant differences in 7 subitems, including copy cube, draw clock, naming, serial 7 subtraction, repeat, fluency and abstraction. The median scores of medical staff in another 5 subitems were consistent with the standard scores. After further training which was focused on the complicated evaluation standard, the differences in revised evaluation method and original English edition and typical mistakes in staff due to the unconsistency of scores in 7 subitems, the consistency of all scores improved significantly except the median and the standard score of abstraction. Conclusion The consistency check combined with multiple teaching methods including teaching in actual scene can improve the quality of clinical teaching practice of neuropsychology.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".