Evidence map›Paper›PMID 40775627›Full record

ArticleBMC medical education2025

Psychometric properties of the Persian version of the training needs assessment for critical care nurses.

Amir Jalali, Mostafa Bafandeh Zendeh, Azam Sharifi, Amirhossein Naghibzadeh, Leila Moradi, Jalal Karimi, Nazanin Mardani Taghvostani, Khalil Moradi

Abstract read
In one paragraph

Article in BMC medical education, 2025. 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

8 authors.

Amir JalaliSubstance Abuse Prevention Research Center, Research Institute for Health, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Mostafa Bafandeh ZendehDepartment of nursing and medical Emergency, Sarab Faculty of Medical Sciences, Sarab, East Azerbaijan, Iran.
Azam SharifiDepartment of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Amirhossein NaghibzadehStudent Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Leila MoradiDepartment of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Jalal KarimiStudent Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Nazanin Mardani TaghvostaniStudent Research Committee, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Khalil MoradiDepartment of Nursing, School of Nursing and Midwifery, Kermanshah University of Medical Sciences, Kermanshah, Iran. moradi.khalil12@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying the training needs of critical care nurses is crucial for enhancing their performance and optimizing patient outcomes. This process requires a comprehensive understanding of their specific knowledge gaps and skill deficiencies. Therefore, a reliable and validated tool is necessary to assess these needs effectively.

aimThis study aimed to assess the psychometric properties of the Persian version of the Training Needs of Critical Care Nurses Scale (TNCCNS) in Iran.

methodsThis cross-sectional methodological study evaluated the Persian version of the TNCCNS using a convenience sample of 676 intensive care unit nurses from hospitals in Kermanshah, Hamadan, and Tabriz. The study was conducted in two phases: translation and cultural adaptation, followed by an assessment of the psychometric properties of the Persian version. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to examine the construct validity of the instrument. Additionally, internal consistency reliability was assessed using Cronbach's alpha and McDonald's omega coefficients, while test-retest reliability was evaluated using the Intraclass Correlation Coefficient (ICC). Data analysis was conducted using SPSS (version 27) and LISREL (version 8). Statistical significance for all analyses was set at p < 0.05.

resultsThe results of the EFA and CFA confirmed the thirteen-factor structure with 66 items, explaining 62.337% of the total variance. CFA indicated a good model fit (RMSEA = 0.056, CFI = 0.92, NNFI / TLI = 0.94, GFI = 0.98, SRMR = 0.046, χ²/df = 2.25). Pearson correlation analysis revealed significant positive relationships between each subscale score and the overall scale score. The scale demonstrated high reliability (Cronbach's alpha = 0.912, McDonald's omega = 0.902) and strong stability (ICC = 0.912, 95% CI: 0.900-0.924).

conclusionsThe Persian TNCCNS has demonstrated robust validity and reliability, establishing itself as a valuable tool for assessing the training needs of ICU nurses in Iran. Its validation carries significant implications for both research and practice, offering a robust framework for evaluating the educational requirements of Iranian critical care nurses and the effectiveness of existing training programs. Furthermore, these findings provide essential guidance for educational administrators and policymakers in designing targeted interventions that enhance nursing competencies and improve patient care outcomes in ICU settings. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

Critical Care NursingNeeds AssessmentAdultClinical CompetenceCross-Sectional StudiesFactor Analysis, StatisticalFemaleHumansIranMaleMiddle AgedPsychometricsReproducibility of ResultsSurveys and QuestionnairesTranslationsCritical careNursePsychometryReliabilityTraining needsValidity

Identifiers

PMID40775627
PMCPMC12330008

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.