Evidence map›Paper›PMID 42388756›Full record

ReviewFrontiers in public health2026

Evidence-based translation in postpartum pelvic floor rehabilitation nursing: recent advances and practical integration.

Yufei Yuan, Huimin Su, Quanyi Long, Yuanyuan Zou

Abstract readReview
In one paragraph

Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yufei YuanDepartment of Obstetrics and Gynecology, The Second Hospital of Kunming, Kunming, China.
Huimin SuDepartment of Internal Medicine, Quanzhou First Hospital, Quanzhou, Fujian, China.
Quanyi LongFaculty of Nursing, Kunming Medical University, Kunming, China.
Yuanyuan ZouDepartment of Neurosciences, Mental Health and Sensory Organs-NESMOS, Sapienza University of Rome, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Postpartum pelvic floor dysfunction (PFD) is a common condition and can undermine women's physical comfort, mental health, and social functioning. As service needs grow, evidence-based translational nursing has been proposed to strengthen postpartum pelvic floor rehabilitation. This review summarizes the current evidence and compares it with routine care. Materials and methods: Recent studies and clinical practice guidelines were examined. Core applications included standardized assessment, pelvic floor muscle training (PFMT), biofeedback/electrical stimulation when indicated, psychological support, health education, and internet-enabled follow-up for continuity of care. Results: Compared with conventional nursing, evidence-based approaches appear to be associated with greater gains in pelvic floor muscle (PFM) strength and lower rates of urinary incontinence and pelvic organ prolapse (POP) in the included literature, with reduced risk of recurrence. Many reports also described improvements in anxiety/depressive symptoms, sexual function, quality of life, satisfaction, and adherence to rehabilitation programmes. Conclusion: By aligning research evidence with clinical judgment and women's preferences, evidence-based translational nursing can deliver more targeted and continuous rehabilitation and support more complete postpartum recovery.

Indexed as

Evidence-Based NursingPelvic Floor DisordersPostpartum PeriodRehabilitation NursingExercise TherapyFemaleHumansPelvic Flooradherencecontinuity of caredigital healthevidence-based nursingpelvic floor muscle trainingpostpartum pelvic floor rehabilitation

Identifiers

PMID42388756
PMCPMC13318617

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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.