PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15181551

Using a modified nominal group technique as a curriculum evaluation tool.

Alison Dobbie·Martin Rhodes·James W Tysinger·Joshua Freeman
View original source ↗Export metadata ↓

Abstract

The modified nominal group technique (NGT) is a useful and practical course evaluation tool that complements existing methods such as evaluation forms, surveys, pretests and posttests, focus groups, and interviews. The NGT's unique contribution to the evaluation process is the semi-quantitative, rank-ordered feedback data obtained on learners' perceptions of a course's strengths and weaknesses. In this paper, we demonstrate through a worked example how to use a modified NGT as a course evaluation tool in medical education.

Explore related subjects

Cohort StudiesCurriculumEducation, MedicalFacultyProgram EvaluationUnited StatesJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Alison Dobbie, Martin Rhodes, James W Tysinger, Joshua Freeman. 2004. Using a modified nominal group technique as a curriculum evaluation tool.. https://pubmed.ncbi.nlm.nih.gov/15181551/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Longitudinal variable selection by cross-validation in the case of many covariates.

Longitudinal models are commonly used for studying data collected on individuals repeatedly through time. While there are now a variety of such models available (marginal models, mixed effects models, etc.), far fewer options exist for the closely related issue of variable selection. In addition, longitudinal data typically derive from medical or other large-scale studies where often large numbers of potential explanatory variables and hence even larger numbers of candidate models must be considered. Cross-validation is a popular method for variable selection based on the predictive ability of the model. Here, we propose a cross-validation Markov chain Monte Carlo procedure as a general variable selection tool which avoids the need to visit all candidate models. Inclusion of a 'one-standard error' rule provides users with a collection of good models as is often desired. We demonstrate the effectiveness of our procedure both in a simulation setting and in a real application.

Cohort Studies↗
PubMed2007

An update on localised prostate cancer.

Prostate cancer remains a most intriguing condition, which poses unique challenges. On reviewing recent literature on localised disease (confined within the capsule T1 or T2, N0, M0), it would seem that it is over treated, resulting in significant morbidity. We now have strong evidence, however, that there can be mortality associated with 'watchful waiting'. Clinicians should, therefore, aim to identify men at risk of progression in order that they should be treated effectively with minimal adverse effects. In this paper, we report on recent developments in the expanding array of management options available.

Cohort Studies↗