Evidence map›Paper›PMID 41583099›Full record

ReviewJournal of Korean gerontological nursing2024

Concept analysis of end-of-life care competency of long-term care hospital nurses: Using a hybrid model.

Sookyeon Son, Mi-Kyeong Jeon

Abstract readReview
In one paragraph

Review in Journal of Korean gerontological nursing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

2 authors.

Sookyeon SonAdjunt Lecturer, Department of Nursing, Changwon National University, Changwon, Korea.ORCID 0000-0002-8950-7451
Mi-Kyeong JeonAssociate Professor, Department of Nursing, Changwon National University, Changwon, Korea.ORCID 0000-0001-6730-2423

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study identified the attributes and indicators of end-of-life care competency in long-term-care hospital nurses and clarified the definition of the concept. Methods: The Competency Outcomes Performance Assessment model was used as a conceptual framework, and conceptual analysis was performed using the hybrid model presented by Schwartz-Barcott and Kim. In the theoretical phase, the attributes of end-of-life care competencies were explored through a literature review. In the fieldwork phase, focus group interview data were analyzed to derive the attributes of nursing competency at the end of life. In the final analysis phase, the attributes and indicators of end-of-life care competency were compared, analyzed, and integrated. Results: The attributes of end-of-life care competency among nurses in long-term care hospitals included comprehensive symptom management, effective communication, situational response, patient-centered care, information provision and education, resource management, demonstrating leadership, and professional development. Conclusion: End-of-life care competency in long-term-care hospital nurses can be defined as a comprehensive set of competencies that includes symptom management, situational adaptation, effective communication, resource utilization, leadership, patient-centered care, meeting the needs of patients' families through adequate information provision and education, and enhancing individual nursing capabilities through professional development. These results can serve as a foundation for developing tools to measure end-of-life care competencies among nurses in long-term care hospitals.

Indexed as

Clinical competenceHospitalsLong-term careNursesTerminal care

Identifiers

PMID41583099
PMCPMC12384292

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.