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Enregistrement W2559640429 · doi:10.4300/jgme-d-16-00540.1

Using Data From Program Evaluations for Qualitative Research

2016· article· en· W2559640429 sur OpenAlexaff
Dorene F. Balmer, Jennifer A. Rama, Maria Athina Martimianakis, Terese Stenfors

Notice bibliographique

RevueJournal of Graduate Medical Education · 2016
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueEvaluation and Performance Assessment
Établissements canadiensThe Wilson CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésComputer scienceQualitative researchConstructiveFlexibility (engineering)Data scienceQualitative propertyQuality (philosophy)Research programManagement scienceProcess (computing)EpistemologySociology

Résumé

récupéré en direct d'OpenAlex

A common question posed to qualitative researchers is, “Can I do qualitative research with the free-text entries from our program's evaluations? There's good feedback in there!” While there may be rich, constructive data as free-text entries on end-of-course or end-of-rotation evaluations, using that text as data for research can present problems when it is collected for program evaluation purposes. This Rip Out describes key distinctions between qualitative research and program evaluation, identifies standards for judging quality in program evaluation, and contrasts these standards with standards for judging quality in qualitative research.Although research and program evaluation are both thorough, systematic inquiries, there is a long-standing debate: Are research and program evaluation theoretically distinct, practically distinct, or one-and-the-same?1 The distinction, if it exists, becomes less clear when available data are qualitative in nature, as when the data are free-text entries on surveys intended for program evaluation. Because educational programs typically consist of dynamic components and unfold in complex, unpredictable contexts, program evaluation may have to adapt over time as program goals are clarified or as interventions give way to new learning.2,3 Thus, inquiry that welcomes complexity, multiplicity, and flexibility seems fitting. In this regard, qualitative research can attend to dynamic social phenomena, such as how educational programs are adapted and become part of routine practice.4 However, there are key differences between research and program evaluation. In this article, we propose that these inquiries are distinct in (1) the issues they address; (2) their intended scope; and (3) the standards they use to judge the quality of the work in general, and the data in particular.Although qualitative research and program evaluation both seek to understand what is happening, they diverge in issues and scope. Qualitative research usually addresses theoretical issues, asking questions such as, “Why is this happening?” Qualitative researchers then seek to locate the answer to the question in a larger body of literature, and to make claims of relevance to that literature. Conversely, program evaluation usually addresses practical issues and asks questions such as, “What is actually happening” or “What should be happening?” Answers to these questions aim to inform strategies for program improvement or judgments about the worth of a program locally, without stringent claims for transferability to other contexts. Qualitative research may occasionally ask, “What should be happening?” and program evaluation may address, “Why is this happening?” However, the general issues addressed and the project's scope are different (table).We also propose that research and program evaluation are distinct because they are held to different standards for judging quality, or stated another way, they use different guiding principles. Standards for methodological rigor in qualitative research include the following: Are the data credible (a proxy for internal validity), transferable (a proxy for external validity), dependable (a proxy for reliability), and confirmable (a proxy for objectivity)?5 Standards for program evaluation have a different methodological focus on practical concerns: Are the data useful for informing decisions, feasible to collect, and accurate representations of stakeholder perspectives?6Medical educators may try, inadvisably, to retrospectively fit responses to free-text questions collected for program evaluation into rigorous standards for qualitative research. This may occur with the desire to disseminate their work in academic journals. For example, rather than describing how information in free-text entries on end-of-rotation evaluations was used to refine an educational program, which would be of interest to evaluators, they focus instead on abundance and credibility of data, of interest to researchers. Similarly, rather than discussing how data collected from interviews with faculty members helped establish the local worth of an online continuing medical education program, which would be of concern to evaluators, they fret about their convenience sampling strategy, of concern to researchers. This is not to say that program evaluation should be conducted less systematically or thoughtfully than research. Rather, the point is that initial decisions about each project will be guided differently: primarily, although not solely, by theoretical issues (research) or by practical issues (program evaluation).Recently, some medical education scholars have proposed that understanding mechanisms underpinning educational processes, such as how learners learn, is akin to program evaluation.2 From this point of view, methodological rigor of research is required to understand the human processes at play. However, orienting the inquiry toward topics relevant to diverse stakeholder groups leads to program evaluation. By way of illustration, the facilitated feedback model of Sargeant et al,7 designed to build relationship, explore reactions and content, and coach for performance change (R2C2), blends research and program evaluation. The authors undertook a qualitative research study to develop the model, but then intentionally refined the model according to feasibility standards from program evaluation.

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

Étiquettes directes de modèles (non validées)

Étiquettes de catégorie et de devis d'étude par modèle, issues des rondes d'étiquetage. C'est une sortie machine, non validée, et le désaccord entre modèles est livré comme donnée. Aucun devis ici n'est encore validé contre MEDLINE.

BrasCatégoriesDevis d'étudeConfiance
gemmaaucune catégorie
Domaine: non disponible · Genre: Méthodes
Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non
Sans objetlow
gptMétarecherche
Domaine: Méthodes · Genre: Méthodes
Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: non
Qualitatiflow
modèles en désaccordL'accord compare des ensembles de catégories et des devis identiques entre les bras.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,059
score de la tête « metaresearch » (Gemma)0,090
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,869
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0590,090
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,952
Tête enseignante GPT0,816
Écart entre enseignants0,136 · 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

Étiqueté directement par 2 modèles lisant le dossier complet.

Métarecherche

Les modèles divergent sur des parties de cette classification; chaque voix est préservée dans la section en fin de page.

Devis d'étudeSans objet · Qualitatif
DomaineMéthodes
GenreMéthodes

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

En bref

Citations14
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

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