Evidence map›Paper›PMID 35078471›Full record

ArticleBMC health services research2022

Evaluating factors that influenced the successful implementation of an evidence-based neonatal care intervention in Chinese hospitals using the PARIHS framework.

Jieya Yue, Jun Liu, Yingxi Zhao, Sarah Williams, Bo Zhang, Lin Zhang, Qiannan Zhang, Xin Liu, Stephen Wall, Gengli Zhao

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
5.8field-weighted citation impact, top 4% of its field
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.

  1. Pooled it
  2. Translation and cultural adaptation to the Brazilian context of the Organizational Readiness to Change Assessment scale.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2026
    Article
  3. Article
  4. Review
  5. Article
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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

10 authors at 6 institutions in 3 countries.

Jieya YuePeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China.
Jun LiuPeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China.
Yingxi ZhaoNuffield Department of Medicine, University of Oxford, Oxford, UK.
Sarah WilliamsSave the Children UK, London, UK.
Bo ZhangPeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China.
Lin ZhangSave the Children, Chengdu, China.
Qiannan ZhangPeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China.
Xin LiuPeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China.
Stephen WallSave the Children US, Washington DC, USA.
Gengli ZhaoPeking University First Hospital, 1 Xi'anmen St, Xicheng, Beijing, China. zhaogengli@sina.com.
Peking University · CNChengdu Women's and Children's Central Hospital · CNPeking University First Hospital · CNSave the Children · USSave the Children · GBUniversity of Oxford · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEvidence based interventions (EBIs) can improve patient care and outcomes. Understanding the process for successfully introducing and implementing EBIs can inform effective roll-out and scale up. The Promoting Action on Research Implementation in Health Services (PARIHS) framework can be used to evaluate and guide the introduction and implementation of EBIs. In this study, we used kangaroo mother care (KMC) as an example of an evidence-based neonatal intervention recently introduced in selected Chinese hospitals, to identify the factors that influenced its successful implementation. We also explored the utility of the PARIHS framework in China and investigated how important each of its constructs (evidence, context and facilitation) and sub-elements were perceived to be to successful implementation of EBIs in a Chinese setting.

methodWe conducted clinical observations and semi-structured interviews with 10 physicians and 18 nurses in five tertiary hospitals implementing KMC. Interview questions were organized around issues including knowledge and beliefs, resources, culture, implementation readiness and climate. We used directed content analysis to analyze the interview transcript, amending the PARIHS framework to incorporate emerging sub-themes. We also rated the constructs and sub-elements on a continuum from "low (weak)", "moderate" or "high (strong)" highlighting the ones considered most influential for hospital level implementation by study participants.

resultsUsing KMC as an example, our finding suggest that clinical experience, culture, leadership, evaluation, and facilitation are highly influential elements for EBI implementation in China. External evidence had a moderate impact, especially in the initial awareness raising stages of implementation and resources were also considered to be of moderate importance, although this may change as implementation progresses. Patient experience was not seen as a driver for implementation at hospital level.

conclusionBased on our findings examining KMC implementation as a case example, the PARIHS framework can be a useful tool for planning and evaluating EBI implementation in China. However, it's sub-elements should be assessed and adapted to the implementation setting.

Indexed as

Kangaroo-Mother Care MethodDelivery of Health CareHealth ServicesHealth Services ResearchHospitalsHumansInfant, NewbornChinaContextCultureEvidence-basedFacilitationImplementationKangaroo mother carePARIHS

Identifiers

PMID35078471
PMCPMC8787972
OpenAlexW4225685925

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.