Evidence map›Paper›PMID 35831867›Full record

ArticleBMC medical education2022

A novel multimodal needs assessment to inform the longitudinal education program for an international interprofessional critical care team.

Heyi Li, Yuqiang Sun, Amelia Barwise, Wenjuan Cui, Yue Dong, Aysun Tekin, Qingzhong Yuan, Lujun Qiao, Ognjen Gajic, Alexander Niven

Abstract read
In one paragraph

Article in BMC medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

10 authors.

Heyi LiDepartment of Medicine, Division of Pulmonary and Clinical Care Medicine, Mayo Clinic, Rochester, MN, USA.
Yuqiang SunDepartment of Emergency Medicine, China Medical University, Shenyang, China.
Amelia BarwiseDepartment of Medicine, Division of Pulmonary and Clinical Care Medicine, Mayo Clinic, Rochester, MN, USA.
Wenjuan CuiDepartment of Intensive Care Medicine, Shengli Oilfield Central Hospital, Dongying, China.
Yue DongDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA.
Aysun TekinDepartment of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, USA.
Qingzhong YuanDepartment of Intensive Care Medicine, Shengli Oilfield Central Hospital, Dongying, China.
Lujun QiaoDepartment of Intensive Care Medicine, Shengli Oilfield Central Hospital, Dongying, China.
Ognjen GajicDepartment of Medicine, Division of Pulmonary and Clinical Care Medicine, Mayo Clinic, Rochester, MN, USA.
Alexander NivenDepartment of Medicine, Division of Pulmonary and Clinical Care Medicine, Mayo Clinic, Rochester, MN, USA. Niven.Alexander@mayo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe current global pandemic has caused unprecedented strain on critical care resources, creating an urgency for global critical care education programs. Learning needs assessment is a core element of designing effective, targeted educational interventions. In theory, multimodal methods are preferred to assess both perceived and unperceived learning needs in diverse, interprofessional groups, but a robust design has rarely been reported. Little is known about the best approach to determine the learning needs of international critical care professionals.

methodWe conducted multimodal learning needs assessment in a pilot group of critical care professionals in China using combined quantitative and qualitative methods. The assessments consisted of three phases: 1) Twenty statements describing essential entrustable professional activities (EPAs) were generated by a panel of critical care education experts using a Delphi method. 2) Eleven Chinese critical care professionals participating in a planned education program were asked to rank-order the statements according to their perceived learning priority using Q methodology. By-person factor analysis was used to study the typology of the opinions, and post-ranking focus group interviews were employed to qualitatively explore participants' reasoning of their rankings. 3) To identify additional unperceived learning needs, daily practice habits were audited using information from medical and nursing records for 3 months.

resultsFactor analysis of the rank-ordered statements revealed three learning need patterns with consensual and divergent opinions. All participants expressed significant interest in further education on organ support and disease management, moderate interest in quality improvement topics, and relatively low interest in communication skills. Interest in learning procedure/resuscitation skills varied. The chart audit revealed suboptimal adherence to several evidence-based practices and under-perceived practice gaps in patient-centered communication, daily assessment of antimicrobial therapy discontinuation, spontaneous breathing trial, and device discontinuation.

conclusionsWe described an effective mixed-methods assessment to determine the learning needs of an international, interprofessional critical care team. The Q survey and focus group interviews prioritized and categorized perceived learning needs. The chart audit identified additional practice gaps that were not identified by the learners. Multimodal methods can be employed in cross-cultural scenarios to customize and better target medical education curricula.

Indexed as

Education, MedicalCritical CareCurriculumHumansLearningNeeds AssessmentCritical careCurricular milestonesDelphiEntrustable professional activityIntensive careLearning needs assessmentMedical continuing educationMedical trainingQ method

Identifiers

PMID35831867
PMCPMC9281106

What OpenQuestion holds

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LicenceCC BY
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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.