Survey on the knowledge level and implementation status of "family centered care" in pediatric nurses
Bibliographic record
Abstract
Objective To conduct a baseline survey on implementation status of family centered care (FCC) by selecting pediatric nurses from 21 comprehensive tertiary hospitals. Methods A total of 420 questionnaires were distributed to 21 comprehensive tertiary hospitals that selected by using convenience sampling method. Consensus matching of nurses' cognitive situation and current nursing situation were evaluated according to the FCC scale of Boson children's hospital in American. Results Among 420 questionnaires that distributed in 21 hospitals, 12 invalid questionnaires were excluded and 408 questionnaires were included in the analysis. Pediatric nurses in first-tier cities performed better compared with the second/third-tier cities on FCC knowledge (χ2=36.68, P<0.001) and implementation status (t=4.41, P<0.001) . Pediatric nurses with bachelor degree or higher education had higher FCC knowledge level than nurses with other educational background (χ2=31.56, P<0.001) , while there was no statistically significant difference in the implementation status among pediatric nurses with different educational background (t=0.957, P=0.340) . Working seniority, academic title and duties affected FCC knowledge level (P<0.05) . Conclusions Knowledge level of FCC is related to development of cities, educational background, academic title and duties of pediatric nurses. Implementation status of FCC is associated with development of cities, and it is not related to educational background. Key words: Nurses; Pediatric department; Family centered care; Humanistic concern; Knowledge level
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".