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 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 | 0.005 | 0.028 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".