MétaCan
Menu
Retour à la cohorte
Enregistrement W2091793466 · doi:10.4300/jgme-d-13-00092.1

How Do You Define High-Quality Education Research?

2013· article· en· W2091793466 sur OpenAlexaboutno aff
Lalena M. Yarris, Deborah Simpson, Gail M. Sullivan

Notice bibliographique

RevueJournal of Graduate Medical Education · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésQuality (philosophy)Data scienceMedical educationComputer scienceMEDLINEMedicinePolitical science

Résumé

récupéré en direct d'OpenAlex

Merriam-Webster defines quality as a degree of excellence.1 What is left unstated is how degree and excellence are defined. Does this suggest that the quality of medical education research, like beauty, lies in “the eye of the beholder?” Can we measure quality objectively and consistently or is it subjective and contextual, varying with the type of research question, reviewers' judgments, or quality indices applied? Do these factors capture the aspects of quality that you, our readers, value? We pose these questions for your consideration as you read the following review papers published in this issue of the Journal of Graduate Medical Education (JGME). Locke and colleagues2 reviewed graduate medical education (GME) research papers published in 2011 and selected the 12 articles they considered to be of the greatest importance to internal medicine teachers. With a similar target audience, Eaton et al3 used the Medical Education Research Study Quality Index (MERSQI)4,5 to score internal medicine residency quantitative research papers over a 2-year period. The authors then reviewed the papers ranking in the top 25th percentile for common themes. Examining papers in the surgical education literature published over a decade, Wohlauer and colleagues6 identified common themes and research methods through reviewing the most frequently cited articles in Web of Science, as a surrogate for relevance and quality. Each review aims to identify notable medical education papers for a specific audience and time period, but each takes a different approach. Despite overlapping themes (common topics were simulation, duty hours, resident well-being or distress, resident assessment, and career choices), these 3 reviews achieved different results. Of note, the reviews by Locke et al3 and Eaton et al2 had comparable target audiences, search techniques, and journals reviewed, yet they identified only 2 common papers. The differences may be explained by the use of dissimilar quality criteria, exclusion of qualitative papers for 1 review and only a 50% overlap in review periods. However, the finding that 2 selection processes with a similar aim resulted in almost mutually exclusive results remains striking. The lack of a common definition of quality for medical education research does not stem from a lack of prior efforts to both define and improve the quality of our studies. In addition to the MERSQI, other instruments exist to measure quality in quantitative studies, such as the Best Evidence in Medical Education Global Scale and the Modified Newcastle-Ottawa Scale.7,8 These instruments vary in their (1) incorporation of items that address methodological rigor, (2) reliance on outcome quality based on Kirkpatrick's hierarchy of outcomes of educational interventions, and (3) their association with quality based on a systematic review of method and reporting quality in education research.9–,11 Although methodological rigor is the foundation of quality, attempts to boost quality by focusing on rigor at the expense of other aspects of quality can sometimes diminish the value of the results for consumers. Even the emphasis on outcomes research, a well-intentioned effort to encourage studies that address the highest tier outcomes (patient care or physician behavior outcomes) may result in the unintended consequences of dilution, diminished feasibility, failure to establish a causal link, biased outcome selection, and “teaching to the test.” 12 In addition, we understand that consumers of education research may place value on factors that are not captured by available instruments and that may be neglected by a myopic focus on only the pinnacle of Kirkpatrick's pyramid. The definition of quality for a given product is usually informed by the consumers of that product. Readers of JGME may value elements of quality that are not currently captured by available instruments or methods; we are seeking your input to guide us in future efforts to identify notable medical education papers and help redefine quality in our research.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,480
score de la tête « metaresearch » (Gemma)0,742
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Évaluation · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,520
Score d'incertitude au seuil0,641

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,4800,742
Méta-épidémiologie (sens strict)0,0030,003
Méta-épidémiologie (sens large)0,0110,004
Bibliométrie0,0290,026
Études des sciences et des technologies0,0080,047
Communication savante0,0600,050
Science ouverte0,0080,016
Intégrité de la recherche0,0230,036
Charge utile insuffisante (le modèle a refusé de juger)0,0060,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,091
Tête enseignante GPT0,451
Écart entre enseignants0,359 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
DomaineÉvaluation
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2013
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Graduate Medical EducationMême sujetInnovations in Medical EducationTravaux en français237 207