Evidence map›Paper›PMID 40707991›Full record

ArticleBMC research notes2025

Explaining the challenges faced by nursing students in clinical learning environments during the post-COVID era: a qualitative content analysis.

Zeinab Jokar, Mostafa Bijani, Hajar Haghshenas

Abstract read
In one paragraph

Article in BMC research notes, 2025. 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
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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

3 authors.

Zeinab JokarDepartment of Operation room, School of Paramedical, Bushehr University of Medical Sciences, Bushehr, Iran.
Mostafa BijaniDepartment of Medical Surgical Nursing, School of Nursing, Fasa University of Medical Sciences, Fasa, Iran. bizhani_mostafa@yahoo.com.
Hajar HaghshenasDepartment of Nursing, School of Nursing, Gerash University of Medical Sciences, Gerash, Iran. Haghshenas@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe COVID-19 pandemic precipitated unprecedented disruptions across global healthcare systems, with nursing education being particularly affected. In addition to the challenges encountered during the height of the crisis, the aftermath of the pandemic has continued to impose significant difficulties on nursing students, especially within clinical learning environments. Despite the breadth of emerging literature, a notable gap persists in studies explicitly examining the post-pandemic experiences of nursing students in these settings. Moreover, the present study is a qualitative study with the content analysis in Iran from October 2024 to February 2025. Twenty- two nursing students participated in this study. Data were collected from individual, and semi-structured interviews. Data analysis was conducted using the qualitative content analysis method proposed by Graneheim and Lundman (2004).

resultsThe analysis yielded three overarching themes, each encompassing specific subthemes: (1) lack of clinical competence (insufficient clinical knowledge, deficiency in clinical skills, and poor clinical judgment.) (2), psychological challenges (the quagmire of hopelessness, emotional exhaustion, and lack of self-efficacy) and (3) disruption in professional interactions (lack of mutual respect and absence of a team-oriented spirit). CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Clinical CompetenceCOVID-19Students, NursingAdultFemaleHumansIranLearningMalePandemicsQualitative ResearchSARS-CoV-2Self EfficacyYoung AdultClinical learning environmentsCOVID-19Nursing studentsQualitative study

Identifiers

PMID40707991
PMCPMC12291244

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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.