Interviewing <i>in situ</i> : employing the guided walk as a dynamic form of qualitative inquiry
Bibliographic record
Abstract
CONTEXT: The purpose of this paper is to provide a critical analysis of a mobile research method, the guided walk, and its potential suitability in medical education research. METHODS: The Northern Ontario School of Medicine's (NOSM) longitudinal integrated clerkship served as the research context in which the guided walk method was used to explore the lived experiences of 12 Year 3 medical students undertaking their clerkship in one of eight different communities across Northern Ontario, Canada. Informed by the social constructivist research paradigm, the guided walk method was employed to answer the research question: how do Year 3 medical students at NOSM describe their clerkship experiences as encountered in their placement and living contexts? Through an inductive thematic analysis of the data, the findings provided a rich description of the guided walk from the participants' and the researcher's perspectives. RESULTS: There were significant advantages to using the guided walk rather than other types of qualitative research approaches. The guided walk made it easier for participants to take part in the study, provided context-rich research interactions, and led to serendipitous encounters for both participants and the first author. There were also challenges and limitations associated with the guided walk method. For example, this method carries inherent challenges with reference to the safeguarding of confidentiality and anonymity for both participants and those encountered during the walk. CONCLUSIONS: The guided walk method is promising within medical education, particularly for researchers seeking to gain participants' stories in the contexts to which they refer. This method may be appropriate for use in medical education research in areas such as the evaluation and assessment of a student's clinical decision-making skills and competency development, as well as the consolidation of strategies to manage ethical and professional dilemmas.
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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 | 0.037 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".