Sanquan education concept on the treatment of students’ recognition of functional cognitive impairment
Bibliographic record
Abstract
Background Many schools have readjusted their teaching management strategies in order to implement the principle of “Sanquan education”, that is, whole-process education and all-round education. The purpose of this study is to understand the influence of Sanquan teaching concept on the identification of students with functional cognitive impairment. Subjects and Methods Patients with cognitive impairment in a school were selected as research objects and randomly divided into a control group and an experimental group. The control group received the traditional teaching management mode, while the experimental group implemented the new teaching management mode that fully implemented the three-in-one education policy. The Montreal Cognitive Assessment Scale was used for assessment and SPSS22.0 was used for statistical analysis. Results After 6 months of experiment, the control group’s cognitive rating scale score changed from 11 to 13 points, while the experimental group’s cognitive rating scale score changed from 12 to 26 points. In the experimental group, the symptoms of patients with functional cognitive impairment were significantly alleviated (P<0.05). The experimental results show that the introduction of the concept of Sanquan education into teaching management has a significant impact on students’ recognition of functional cognitive impairment. Conclusions The introduction of the “ Sanquan education” policy in school teaching management can have a positive impact on the identification of students with functional cognitive impairment, reduce the symptoms of patients, and provide a potential therapeutic method with research significance. The results of this study provide a reference for school management and treatment of mental illness.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".