Platon analysis method applied in nursing satisfaction survey released the follow-up system
Bibliographic record
Abstract
Objective:To investigate Platon analysis method in the follow-up care satisfaction survey application,improved patient's satisfaction with nursing care of adverse factors,improve the quality of nursing service.Methods:self-designed follow-up care satisfaction investigation content,application of follow-up system information platform,to discharge 3 days in patients with follow-up messages sent,information platform automatically accept patients answer message,personal information collation,collection,will not be satisfactory project is drawn into the Platon statistics,analysis of adverse factors,formulate measures for improvement.Results:for the first quarter a return visit information 9 071,reply message 3 531,recovery rate is 38.93%,with 93,not satisfied with the rate of 2.63%.Third quarter issued return information 10 309,reply message 4 325,recovery rate is 41.95%,with 54,not 1.25% satisfaction rate,cumulative percentage dropped to 58.06%,to improve the effect of 41.94%.Conclusion:the application of Platon analysis of patients' satisfaction with nursing care of adverse factors,find the key problem,take targeted measures,improve nursing service quality,which is beneficial for the patient to provide high quality of nursing service.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".