Evidence map›Paper›PMID 41404556›Full record

ArticleFrontiers in public health2025

A quantitative study of China's rehabilitation policies from the perspective of policy tools.

Xinyang Jiang, Li Luo

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Xinyang JiangSchool of Public Health, Fudan University, Shanghai, China.
Li LuoSchool of Public Health, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Rehabilitation policies constitute a critical guiding framework for national and regional development. This study aims to use policy tools to analyze the specific content and structural characteristics of China's rehabilitation-related policies from 2010 to 2023, and provide a new reference perspective for international rehabilitation policy research. Materials and methods: A three-dimensional analytical framework of "policy tool type-policy theme characteristic-stakeholder" was developed. Data were drawn from nation-level policies on rehabilitation medical services issued by the State Council of the People's Republic of China and the National Health Commission. Based on established inclusion and exclusion criteria, 41 valid policy documents were selected. NVivo (release 14.23.1) was used for four-level coding, and reliability and validity tests were conducted. Policy texts were first quantified, followed by descriptive analysis. Results: A total of 298 codes were identified. Among policy tool types, environmental tools accounted for the highest proportion (58.05%), followed by supply-side tools (32.98%), while demand-side tools had the lowest proportion (9.06%). From the stakeholder perspective, provider organizations (42.95%) were the core target, followed by regulatory authorities (19.13%) and rehabilitation service recipients (19.80%). Clinical practitioners and the rehabilitation capital market each accounted for 9.06%, with most of the policies related to passive response-type policies. Conclusion: China's current rehabilitation medical service policies have insufficient use of supply-side and demand-side tools, and uneven policy coverage among stakeholders. The structural application of policy tools should be optimized, particularly by strengthening the role of demand-side policy tools.

Indexed as

Health PolicyPolicy MakingRehabilitationChinaHumansChinapolicy analysispolicy toolrehabilitationrehabilitation policy

Identifiers

PMID41404556
PMCPMC12702861

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.