Evidence map›Paper›PMID 25551678›Full record

ArticleBMC medical education2014

Reliability of the interprofessional collaborator assessment rubric (ICAR) in multi source feedback (MSF) with post-graduate medical residents.

Mark F Hayward, Vernon Curran, Bryan Curtis, Henry Schulz, Sean Murphy

Abstract read
In one paragraph

Article in BMC medical education, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

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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

11 citing papers in PubMed.

  1. Evidence-informed propositions for a whole-of-program assessment approach.Nutrition & dietetics : the journal of the Dietitians Association of Australia · 2025
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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

5 authors.

Mark F Hayward
Vernon Curran
Bryan Curtis
Henry Schulz
Sean Murphy

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreased attention on collaboration and teamwork competency development in medical education has raised the need for valid and reliable approaches to the assessment of collaboration competencies in post-graduate medical education. The purpose of this study was to evaluate the reliability of a modified Interprofessional Collaborator Assessment Rubric (ICAR) in a multi-source feedback (MSF) process for assessing post-graduate medical residents' collaborator competencies.

methodsPost-graduate medical residents (n = 16) received ICAR assessments from three different rater groups (physicians, nurses and allied health professionals) over a four-week rotation. Internal consistency, inter-rater reliability, inter-group differences and relationship between rater characteristics and ICAR scores were analyzed using Cronbach's alpha, one-way and two-way repeated measures ANOVA, and logistic regression.

resultsMissing data decreased from 13.1% using daily assessments to 8.8% utilizing an MSF process, p = .032. High internal consistency measures were demonstrated for overall ICAR scores (α = .981) and individual assessment domains within the ICAR (α = .881 to .963). There were no significant differences between scores of physician, nurse, and allied health raters on collaborator competencies (F2,5 = 1.225, p = .297, η2 = .016). Rater gender was the only significant factor influencing scores with female raters scoring residents significantly lower than male raters (6.12 v. 6.82; F1,5 = 7.184, p = .008, η 2 = .045).

conclusionThe study findings suggest that the use of the modified ICAR in a MSF assessment process could be a feasible and reliable assessment approach to providing formative feedback to post-graduate medical residents on collaborator competencies.

Indexed as

Clinical CompetenceCooperative BehaviorEducation, Medical, GraduateFeedbackInternship and ResidencyEducational MeasurementFemaleHumansInterprofessional RelationsMaleReproducibility of Results

Identifiers

PMID25551678
PMCPMC4318203

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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.