Digital Artifacts of Self‐Representation: A Critical Qualitative Analysis of Nursing Memes
Bibliographic record
Abstract
Stereotypes in mass media depict harmful and inaccurate portrayals of nurses and nursing work. As memes are understood to be units of culture, they may be examined as artifacts, deepening understandings of contemporary culture. This critical qualitative analysis of nursing memes from two popular social media platforms seeks to identify current cultural narratives and social meanings of nursing reproduced within the public domain. Memes were selected from popular hashtags and nursing meme accounts with more than 2500 followers. Memes were included if they followed traditional meme format and content-centered discourses of gender, race, and other aspects of power and oppression within nursing and healthcare systems. Our analysis employed a qualitative descriptive design within an overarching critical social theoretical framework. We identified that nursing memes reproduced stigmatizing and discriminatory narratives of patients and perpetuated harmful notions of "who" nurses are and "what" nurses do, while also drawing attention to systemic challenges facing the profession. Memes therefore serve as a valuable artifact for communicating contemporary cultural narratives about nursing and nursing work. Generating and distributing memes to raise awareness of systemic pressures may serve as a valuable social strategy toward advocating for systemic shifts in nursing and healthcare to address persistent challenges.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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 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".