亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

School Neighborhood Disadvantage as a Predictor of Long-Term Sick Leave Among Teachers: Prospective Cohort Study

病假 置信区间 人口学 住所 泊松回归 医学 弱势群体 比率 前瞻性队列研究 危险系数 老年学 环境卫生 人口 经济 社会学 外科 内科学 经济增长 物理疗法
作者
Mikko Virtanen,Mika Kivimäki,Jaana Pentti,Tuula Oksanen,Kirsi Ahola,Anne Linna,Anne Kouvonen,Paula Salo,Jussi Vahtera
出处
期刊:American Journal of Epidemiology [Oxford University Press]
卷期号:171 (7): 785-792 被引量:34
标识
DOI:10.1093/aje/kwp459
摘要

This ongoing prospective study examined characteristics of school neighborhood and neighborhood of residence as predictors of sick leave among school teachers. School neighborhood income data for 226 lower-level comprehensive schools in 10 towns in Finland were derived from Statistics Finland and were linked to register-based data on 3,063 teachers with no long-term sick leave at study entry. Outcome was medically certified (>9 days) sick leave spells during a mean follow-up of 4.3 years from data collection in 2000–2001. A multilevel, cross-classified Poisson regression model, adjusted for age, type of teaching job, length and type of job contract, school size, baseline health status, and income level of the teacher's residential area, showed a rate ratio of 1.30 (95% confidence interval: 1.03, 1.63) for sick leave among female teachers working in schools located in low-income neighborhoods compared with those working in high-income neighborhoods. A low income level of the teacher's residential area was also independently associated with sick leave among female teachers (rate ratio = 1.50, 95% confidence interval: 1.18, 1.91). Exposure to both low-income school neighborhoods and low-income residential neighborhoods was associated with the greatest risk of sick leave (rate ratio = 1.71, 95% confidence interval: 1.27, 2.30). This study indicates that working and living in a socioeconomically disadvantaged neighborhood is associated with increased risk of sick leave among female teachers.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ezekiet完成签到 ,获得积分10
3秒前
5秒前
大方的星星完成签到,获得积分10
8秒前
Hcc完成签到 ,获得积分10
9秒前
wujiwuhui完成签到 ,获得积分10
10秒前
甜美的谷云完成签到 ,获得积分10
10秒前
bkagyin应助张泽崇采纳,获得10
14秒前
CipherSage应助Yoyoyuan采纳,获得10
14秒前
科研通AI6.2应助南城采纳,获得10
25秒前
26秒前
我是老大应助微笑采纳,获得10
27秒前
Word麻鸭完成签到,获得积分10
29秒前
乐乐应助个性冰海采纳,获得10
30秒前
33秒前
34秒前
34秒前
清蒸第一大可爱完成签到 ,获得积分10
34秒前
呆萌的仇天完成签到,获得积分10
35秒前
微笑发布了新的文献求助10
39秒前
牛马完成签到,获得积分10
40秒前
丘比特应助哒哒哒采纳,获得10
40秒前
41秒前
42秒前
清爽的罡给高高雪瑶的求助进行了留言
43秒前
li完成签到,获得积分10
44秒前
个性冰海发布了新的文献求助10
47秒前
48秒前
Aulalala完成签到,获得积分10
50秒前
50秒前
52秒前
哒哒哒发布了新的文献求助10
52秒前
张泽崇发布了新的文献求助10
57秒前
愉快的孤晴完成签到,获得积分10
59秒前
独特的师发布了新的文献求助30
1分钟前
1分钟前
研友_VZG7GZ应助morena采纳,获得10
1分钟前
研友_yLpQrn完成签到,获得积分10
1分钟前
1分钟前
斯文败类应助科研通管家采纳,获得10
1分钟前
充电宝应助科研通管家采纳,获得30
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6042149
求助须知:如何正确求助?哪些是违规求助? 7788649
关于积分的说明 16236687
捐赠科研通 5188067
什么是DOI,文献DOI怎么找? 2776201
邀请新用户注册赠送积分活动 1759312
关于科研通互助平台的介绍 1642757