Evidence map›Paper›PMID 32652990›Full record

ArticleBMC health services research2020

Quality analysis of discharge instruction among 602 hospitalized patients in China: a multicenter, cross-sectional study.

Miao-Miao Yang, Wei Liang, Hui Hua Zhao, Ying Zhang

Abstract readMulticenter Study
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Miao-Miao YangDepartment of nursing, Zhongshan Hospital Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Wei LiangDepartment of nursing, Zhongshan Hospital Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Hui Hua ZhaoDepartment of nursing, Zhongshan Hospital Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China. zhh20417@126.com.
Ying ZhangDepartment of nursing, Zhongshan Hospital Fudan University, 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.

Funding

Shanghai Municipal Population and Family Planning Commission 201840116
6 · The paper itself

Abstract

backgroundThe aim of this study was to understand the quality of discharge guidance for patients with chronic diseases, to clarify the gap between patient needs and the content of discharge guidance, and to provide a reference for health education and clinical path management of patients with chronic diseases in the future.

methodsA total of 602 inpatients with stroke, coronary heart disease, cancer, chronic obstructive pulmonary disease and diabetes from the chronic disease-related departments of 7 tertiary general hospitals in China were selected by convenience sampling. Measures included a demographic questionnaire and the Quality of Discharged Teaching Scale(QDTS). Descriptive analysis ANOVA and paired t-test were completed by SPSS 22.0 software.

resultsThe overall average score of QDTS in this survey was 155.79 ± 23.29. The total score of QDTS in chronic obstructive pulmonary disease was lower than coronary heart disease (P < 0.001) and cancer (P = 0.02). While coronary heart disease was higher than stroke (P = 0.01) and diabetes (P = 0.01). And the scores of patients on discharge guidance skills and effects were higher than 8.50.

conclusionsThe level of the patients' perception of quality of discharge insrtuction is middle to high. Managers should understand the characteristics of various departments, give corresponding guidance and help, and clinical nurses should understand the characteristics of ward patients and pay more attention to individual guidance.

Indexed as

Patient DischargePatient Education as TopicQuality of Health CareAdolescentAdultAgedAged, 80 and overChinaChronic DiseaseCross-Sectional StudiesFemaleHospitalizationHospitals, GeneralHumansMaleMiddle AgedChronic diseaseDischargeEducationQuality

Identifiers

PMID32652990
PMCPMC7353724

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.