Qualitative ego networks in health professions education: Capturing the self in relation to others
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Our very sense of self emerges through interactions with others. As part of this State of the Science series on Self, Society, and Situation, we introduce a qualitative ego network research approach. This research approach offers insights into the self's (the ego's) interpretation of and relation to named others in the social network in question. PURPOSE: Visual mapping of participants' social networks is gaining traction, yet this research approach has received no focused attention in the health professions education (HPE) literature. A qualitative ego network approach is a compelling research approach because it uniquely maps participants' perceptions of the complex social world they are embedded in. Although many methodologies can explore participants' social world, ego networks can enhance expression of tacit knowledge of one's social environment and encourage reflection. This approach, combined with other qualitative data, can also reveal hidden relational data that the researcher may not observe or consider. To demonstrate its value as a visual methodology, we will showcase two examples of qualitative ego network studies. We then balance the paper with some critical reflections of this research approach. CONCLUSIONS: A qualitative ego network approach holds potential for deepening understanding of the self in relation to society and situation in future HPE research. We look forward to intentional, impactful and invigorated research using a qualitative ego network approach as we tackle unknowns about how self and society in specific HPE situations interact.
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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 | 0.002 | 0.000 |
| 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) | 0.003 | 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 it