Qualitative study on content of preoperative anesthesia education for patient with general anesthesia
Bibliographic record
Abstract
Objective To understand the advice of anesthesia experts on the content of preoperative anesthesia education for patient with general anesthesia, and to provide reference for clinical practice. Methods Qualitative descriptive research was used, from January to March 2017, and 10 anesthesia specialists from a Class Ⅲ Grade A hospital were selected by purposive sampling, and received semi structured interview. After recording and transcripting, the data were analyzed by using conventional content analysis with help of QSR Nvivo8.0. Results The recommendations for health education of general anesthesia patients before anesthesia included 5 aspects: basic introduction of anesthesia, risk of general anesthesia, preoperative anesthesia preparation, experience of anesthesia on surgery-day, and postoperative awaking or recovery experience. Conclusions Healthcare providers need to strengthen the basic knowledge of anesthesia education, refine the content of education on the day of surgery, focusing on the risks and benefits of general anesthesia and perioperative anesthesia information, so as to provide a complete and comprehensive preoperative anesthesia education for patient with general anesthesia. Key words: Anesthesia; Health education; Preoperative care; Qualitative study
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".