Using Data From Program Evaluations for Qualitative Research
Bibliographic record
Abstract
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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.059 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".