Digital Storytelling: Navigating Individual and Emerging Professional Selves Through Medical Memes
Bibliographic record
Abstract
Background Socialization is a critical process for professional identity formation (PIF), but little is known about how this occurs through online engagement. Objective To explore how Taiwanese medical trainees form professional identities on social media through engagement with medical memes. Methods Using a conceptual framework that examined the dual focus of PIF as a negotiation between personal and professional selves, a descriptive qualitative analysis of medical memes was implemented. In total, 369 memes from a resident’s popular Facebook fan page were analyzed using content analysis. Textual and visual elements of memes were analyzed to understand how trainees expressed their individuality, how they conformed to professional norms, and the tensions they experienced. Eighteen codes emerged and were categorized into levels of individual, relational, organizational, and societal. Comments were also analyzed to understand respondents’ engagement with memes and support of the PIF process. Results Findings show insights into how medical trainees navigate the dual focus of PIF on multiple levels: the individual and the collective (relational, organizational, and societal). Trainees’ engagement with memes highlighted the identity dissonance they experienced as they negotiated losing parts of their individual selves while learning how to conform to organizational norms. Memes allow trainees to reflect on how they experience misalignments between what they expect from the learning environment and workplace realities. Conclusions Online socialization through engagement with medical memes supports the development of PIF; however, it also provides space for trainees to express resistance, share negative experiences, and gain peer solidarity during difficult professional transitions.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".