Why Open-Ended Survey Questions Are Unlikely to Support Rigorous Qualitative Insights
Bibliographic record
Abstract
Health professions education researchers are increasingly relying on a combination of quantitative and qualitative research methods to explore complex questions in the field. This important and necessary development, however, creates new methodological challenges that can affect both the rigor of the research process and the quality of the findings. One example is "qualitatively" analyzing free-text responses to survey or assessment instrument questions. In this Invited Commentary, the authors explain why analysis of such responses rarely meets the bar for rigorous qualitative research. While the authors do not discount the potential for free-text responses to enhance quantitative findings or to inspire new research questions, they caution that these responses rarely produce data rich enough to generate robust, stand-alone insights. The authors consider exemplars from health professions education research and propose strategies for treating free-text responses appropriately.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.455 | 0.742 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".