Sliding Doors: Pivotal Research Decisions and Their Influence on Exploring Sensitive Topics
Bibliographic record
Abstract
This paper is the third in a series that qualitatively explores sensitive topics in health professions education (HPE). Here, our purpose is to consider how researchers' topical, methodological, and theoretical design choices create myriad up and downstream effects impacting study processes, outcomes, and - ultimately - implications for practice. Specifically, this paper uses the Sliding Doors metaphor as a thought exercise to consider alternate research paths for Geringer and colleagues' exploration of the imposter phenomenon (Paper 2), contemplating the what ifs had authors used a different methodology, incorporated other theoretical lenses, or chose to focus their exploration of self-assessment from an entirely different vantage point. By imagining how design decisions at critical junctures of the research journey simultaneously open and close doors to different ways of understanding, we aim to both highlight the richness and complexity of qualitative inquiry, and inspire researchers to consider the multiple pathways available for exploring sensitive topics in ways that are rigorous, creative, and ethically sound.
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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 | 0.343 | 0.420 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.020 | 0.092 |
| Scholarly communication | 0.032 | 0.039 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".