Exploration Self-direct Learning Methods Based on the Training of Medical Interns' Self-direct Learning Ability
Bibliographic record
Abstract
To study the self-direct learning methods in the development of self-direct learning ability of medical interns and analyze the influencing factors. This study included 150 medical interns were selected and divided into a control group and an experimental group. The interns in the traditional mode are the control group, and the interns in the practice group rotation system are the experimental group. There are 75 medical students in each group. And there is no significant difference in the general data of teachers' professional titles, education and teaching qualifications (P>0.05). Before the experiment, there was no significant difference in the total scores of self-direct learning and thinking abilities of the two groups of medical interns. After the experiment, the total scores of self-direct learning and thinking abilities of the experimental group were higher than those of the control group, and the difference was statistically significant (p<0.05). After the internship, the satisfaction and assessment scores of the interns in the experimental group were higher than those in the control group, and the difference was statistically significant (p<0.05). Finally, during the clinical practice period, we should pay attention to cultivating ability of the independent learning and thinking of medical interns, and the application of the practice group rotation system can promote students' independent learning and thinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".