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Record W4411405408 · doi:10.1111/tct.70124

Sliding Doors: Pivotal Research Decisions and Their Influence on Exploring Sensitive Topics

2025· article· en· W4411405408 on OpenAlexaff
Kori A. LaDonna, Paula Cameron, Jamie Geringer, Anna MacLeod, Jerusalem Merkebu

Bibliographic record

VenueThe Clinical Teacher · 2025
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsDoorsMetaphorPhenomenonEngineering ethicsQualitative researchFocus (optics)PsychologyComputer scienceData scienceManagement scienceEpistemologySociologySocial scienceEngineering

Abstract

fetched live from OpenAlex

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.

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

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)0.005
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.398
GPT teacher head0.542
Teacher spread0.144 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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