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

Using Data From Program Evaluations for Qualitative Research

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

Bibliographic record

VenueJournal of Graduate Medical Education · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsComputer scienceQualitative researchConstructiveFlexibility (engineering)Data scienceQualitative propertyQuality (philosophy)Research programManagement scienceProcess (computing)EpistemologySociology

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.059
metaresearch head score (Gemma)0.090
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.090
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.952
GPT teacher head0.816
Teacher spread0.136 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Qualitative
DomainMethods
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2016
Admission routes1
Has abstractyes

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