Evidence map›Paper›PMID 34120415›Full record

ArticleNursing open2022

Benefit-finding experiences of cervical cancer survivors in rural Yunnan province, China: A qualitative study.

Liyuan Sun, Ke Liu, Xilin Li, Yao Zhang, Zhilan Huang

Abstract read
In one paragraph

Article in Nursing open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Revealing relationships: The role of self-disclosure and marital intimacy in the post-traumatic growth of cervical cancer patients-A qualitative study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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  8. Benefit Finding and Related Factors of Patients with Early-Stage Cancer in China.International journal of environmental research and public health · 2022
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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

5 authors.

Liyuan SunHealth Science Center, Shenzhen University, Shenzhen, China.ORCID 0000-0003-2043-2911
Ke LiuHealth Science Center, Shenzhen University, Shenzhen, China.
Xilin LiHealth Science Center, Shenzhen University, Shenzhen, China.
Yao ZhangHealth Science Center, Shenzhen University, Shenzhen, China.
Zhilan HuangHealth Science Center, Shenzhen University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo explore and describe cervical cancer patients' benefit-finding experiences in rural China.

designThe phenomenological approach was used in this qualitative study.

methodsIn-depth, semi-structured interviews were conducted with 21 patients, from July to August of 2019. The data, which were collected, were analysed using the Colaizzi analysis procedure.

resultsAccording to the analysis results, the four benefit-finding experience themes of the cervical cancer patients were, respectively, determined to be as follows: Health cognition and behaviour changes; reshaping of personal strength; focussing on relationships with others; and facing life positively.

conclusionsThe individual's cognition of health and their own strong qualities, and their external social support systems and information obtained and transmitted through interpersonal interactions, can promote the benefit finding. The findings suggest that medical staff should understand the traditional concepts of their patients, use their positive psychological potential, and build targeted nursing measures.

Indexed as

Cancer SurvivorsUterine Cervical NeoplasmsChinaFemaleHumansQualitative ResearchSurvivorsbenefit findingcervical cancerChinese culturequalitative study

Identifiers

PMID34120415
PMCPMC9584485

What OpenQuestion holds

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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.