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Record W4206170721 · doi:10.1016/j.lanwpc.2021.100355

Evidence-based practice implementation in healthcare in China: a living scoping review

2022· article· en· W4206170721 on OpenAlexaff
Junqiang Zhao, Wenhui Bai, Qian Zhang, Yujie Su, Jinfang Wang, DU Xiao-ning, Yajing Zhou, Chang gi Kong, Yanbing Qing, Shaohua Gong, Meiqi Meng, Changyun Wei, Dina Li, Wu Jian, Xuejing Li, Wenjun Chen, Jiale Hu

Bibliographic record

VenueThe Lancet Regional Health - Western Pacific · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHealth careChinaMEDLINESystematic reviewMedicineNursingMedical educationPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Evidence-based practice (EBP) implementation plays a crucial role in bridging the knowledge-action gaps and reducing health inequities. Little is known about its development in China. This study aims to provide an overview of the EBP implementation research progress in healthcare in China and identify gaps for future studies. METHODS: We conducted a scoping review following the Joanna Briggs Institute scoping review methodology and the Cochrane Collaboration's guidance on living reviews. We performed a literature search in four Chinese databases (i.e., China National Knowledge Infrastructure, Wan Fang Database, The VIP Database, and China Biology Medicine) and three English databases (i.e., Ovid MEDLINE, the Cumulative Index to Nursing and Allied Health Literature, and EMBASE), Google scholar, and Baidu scholar from 1996 to 2021. We included EBP implementation studies conducted in healthcare settings in China and were published in Chinese and English literature. The search will be run on a regular basis to monitor the development of new literature and determine when to update the review. FINDINGS: Of the 11,276 records identified, we finally included 309 papers. The publications were on a sharp rise since 2013 and were predominantly from the nursing field (292/309, 94.50%). The commonly researched areas were symptom management (75/309, 24.27%), tube care (46/309, 14.89%), perioperative care (43/309, 13.92%), and fundamental care (43/309, 13.92%). Joanna Briggs Institute model was the most frequently used model to guide the implementation process (92/159, 59.75%). A median number of 8 people often comprised an implementation team, with 113 studies (36.57%) taking a multidisciplinary approach. 204 studies reported utilizing audit criteria to assist evaluation of evidence implementation rate with diversified methods measuring the criteria. Lack of knowledge, skills, and resources, and incomplete procedures or pathways were top barriers impeding EBP implementation. Leadership support was considered the most common facilitator. Education and training were the most frequently described implementation strategies for healthcare professionals and patients. Optimizing workflows and developing evaluation tools were the primary strategies adopted by organizations. 291 studies measured patient outcomes and 174 studies measured healthcare professional outcomes. INTERPRETATION: To our knowledge, this scoping review is the first one to systematically examine the EBP implementation research progress in healthcare in China. Based on this review, we identified contributions that Chinese EBP implementation research made to the global community, and provided eight recommendations for Chinese researchers in conducting implementation studies in the future. FUNDING: None.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.052
metaresearch head score (Gemma)0.090
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.052
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.090
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0290.030
Science and technology studies0.0030.002
Scholarly communication0.0070.006
Open science0.0030.004
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.643
GPT teacher head0.672
Teacher spread0.030 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2022
Admission routes1
Has abstractyes

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