意大利儿科医生儿童睡眠障碍管理认知、态度、实践和满意度的潜在类别分析应用

数据集获取（Figshare）

• 源站标题：Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy
• DOI：10.1371/journal.pone.0228377
• 发布日期：2020-02-03
• 文件数：3 个
• 文件总体积：313.34 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Application_of_latent_class_analysis_in_assessing_the_awareness_attitude_practice_and_satisfaction_of_paediatricians_on_sleep_disorder_management_in_children_in_Italy/11795199

命令行下载

curl -L -o "pone.0228377.s001.doc" "https://ndownloader.figshare.com/files/21525975"
curl -L -o "pone.0228377.s002.doc" "https://ndownloader.figshare.com/files/21525978"
curl -L -o "pone.0228377.s003.doc" "https://ndownloader.figshare.com/files/21525981"

文件清单

• pone.0228377.s001.doc（158.72 KB）
• pone.0228377.s002.doc（114.69 KB）
• pone.0228377.s003.doc（39.94 KB）

文件总体积 313.34 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Aim
To identify subgroups regarding paediatricians’ awareness, attitude, practice and satisfaction about management of Sleep-Disordered Breathing (SDB) in Italy using Latent Class Analysis (LCA).
Methods
A cross-sectional study was conducted on a large sample of Italian paediatricians. Using a self-administered questionnaire, the study collected information on 420 Paediatric Hospital Paediatricians (PHPs) and 594 Family Care Paediatricians (FCPs). LCA was used to discover underlying response patterns, thus allowing identification of respondent groups with similar awareness, attitude, practice and satisfaction. A logistic regression model was used to investigate which independent variables influenced latent class membership. Analyses were performed using R 3.5.2 software. A p-valueResults
Two classes were identified: Class 1 (n = 368, 36.29%) “Untrained and poorly satisfied” and Class 2 (n = 646, 63.71%) “Trained and satisfied.” Involving paediatric pneumologists or otorhinolaryngologists in clinical practice was associated with an increased probability of Class 2 membership (OR = 5.88, 95%CI [2.94–13.19]; OR = 15.95, 95% CI [10.92–23.81] respectively). Examining more than 20 children with SDB during the last month decreased the probability of Class 2 membership (OR = 0.29, 95% CI [0.14–0.61]). FCPs showed a higher probability of Class 2 membership than PHPs (OR = 4.64, 95% CI [3.31–6.55]).
Conclusions
These findings suggest that the LCA approach can provide important information on how education and training could be tailored for different subgroups of paediatricians. In Italy standardized educational interventions improving paediatricians’ screening of SDB are needed in order to guarantee efficient management of children with SDB and reduce the burden of disease.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0228377（source: https://plos.figshare.com/articles/dataset/Application_of_latent_class_analysis_in_assessing_the_awareness_attitude_practice_and_satisfaction_of_paediatricians_on_sleep_disorder_management_in_children_in_Italy/11795199）。