The development and effect evaluation of on-line probation in obstetrics and gynecology nursing in the context of Covid-19
Bibliographic record
Abstract
To explore the effect of network-based on-line probation in obstetrics and gynecology during the special period of Covid-19, the convenience sampling method selected 71 three-year sophomore nursing students as the research object, carried out online probation on TengXun conference platform, and used self-designed teaching effect evaluation questionnaire to evaluate the teaching effect./After probation, students' satisfaction on probation time arrangement, content arrangement, teaching preparation, teaching methods adopted, college teachers and clinical teachers were 90.14%, 92.96%, 98.59%, 97.18%, 94.37%, 94.37%, respectively. The overall evaluation of the completion of learning objectives and online probation reached 92.96% and 94.37%, respectively. Nursing students have a high degree of satisfaction with the teaching effect of online probation in obstetrics and gynecology nursing.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".