Evidence map›Paper›PMID 37332177›Full record

ArticleNursing open2023

The work status of nurses in long-term care institutions in elderly care: A qualitative descriptive study.

Menglin Jiang, Jing Zeng, Mingshu Liao, Quanlei Li

Abstract read
In one paragraph

Article in Nursing open, 2023. 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
–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

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3 · Its place in the literature

Who cites it

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

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4 · The record

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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.

Menglin JiangWest China Xiamen Hospital of Sichuan University, Amoy, China.ORCID 0000-0002-1946-8987
Jing ZengChengdu Medical College, Chengdu, China.ORCID 0000-0001-7049-6142
Mingshu LiaoChengdu Medical College, Chengdu, China.ORCID 0000-0002-2763-1422
Quanlei LiSchool of Nursing, Johns Hopkins University, Baltimore, Maryland, USA.ORCID 0000-0001-5193-3247

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimTo understand the work status of nurses in long-term care institutions in China for elderly care to provide a reference for further formulating management strategies and promoting the development of long-term care teams.

methodsUsing qualitative descriptive research methods, 31 nurses from three long-term care institutions were selected through purposive sampling for in-depth interviews, and a three-week participatory observation was conducted on the daily work of nurses in the above three long-term care institutions. Content analysis was used to analyse data.

resultsIn our sample, nurses in long-term care institutions had insufficient manpower, generally had low academic qualifications, and lacked professional ability. Their work enthusiasm and initiative need to be further improved. Long-term care nurses were moderately paid, and their salary satisfaction was lower than in other trades. At the same time, the social understanding of the long-term care industry was insufficient, and the social identity of nurses in long-term care institutions was low.

conclusionThe development of long-term care requires the joint efforts of nurses, medical institutions, and society. By improving the system, cultivating talents and building a harmonious atmosphere, we aim to enhance the work enthusiasm of long-term care nurses and promote the stable and orderly development of the long-term care team. IMPLICATIONS FOR NURSING MANAGEMENT: Nurses in long-term care institutions are at the core of the ageing age and play a vital role in coping with the ageing problem, meeting long-term care needs, improving the quality of life of old people and reducing the cost of long-term care. The training and management of nurses in long-term care institutions and the construction of the entire long-term care system should be based on China's national conditions and actual needs.

Indexed as

Long-Term CareNursesAgingHumansQualitative ResearchQuality of Lifelong-term carenursequalitative researchthe elderlywork status

Identifiers

PMID37332177
PMCPMC10416048

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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.