Trace and evaluation systems for health services quality in rural and remote areas: a systematic review
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To provide a systematic review of the existing theory, framework, systems and instruments for tracing and evaluating quality in rural health services.
Subjects and methods
We searched six electronic databases up to March 2016. Observational studies of quality assessment of rural health services using theoretical models were included. Ekman’s scale was used to evaluate the quality of the included studies.
A total of 18 studies, published between 2001 and 2015, met the inclusion criteria. The corresponding authors for most of them (7, 44%) are from Chinese institutions and three (3, 17%) from Australian institutions. Five studies (28%) focused on township hospitals. Primary health care quality was reported in five studies (28%), followed by clinical service in four (22%). More than half of the studies (61%) were considered of high quality, and the remainder was of moderate quality. These studies applied 16 theoretical systems, including the model/pattern (4, 25%), method/tool (7, 44%) and framework of the theory (5, 31%). Most of the theoretical models (14, 88%) obtained positive observations. In addition, the conceptual model (6, 36%) and TOPSIS method (2, 13%) were more frequently reported.
Although most of the current studies were considered to have high-quality and positive results, there were limitations in the number of publications and research on theoretical systems. The lacks of unified standards and comprehensive evaluation are important issues that need to be pointed out and resolved.
KeywordsRural health services Quality trace and evaluation Theoretical model Framework Systematic review
This work was supported by the China Medical Board (CMB, grant no. CMB 10–029) and supported by the Fundamental Research Funds for the Central Universities (16LZUJBWTD013): Evidence-based sociology research. The views expressed are those of the authors and not necessarily those of the CMB. The funders had no role in the study design, data collection, data synthesis, data interpretation or writing of the report.
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Data sharing is not applicable to this article as no data sets were generated or analyzed during the current study. If you do not wish to publicly share your data, please write: “Please contact the author for data requests.”
The authors declare that they have no competing interests.
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