Nursing Activism in the Era of Social Media
Bibliographic record
Abstract
ABSTRACT\nBackground: Nurses' imperative to address social injustices must compel the profession to identify new ways to facilitate nurses' activism. Social media engages and connects users and increasingly shapes political landscapes, giving rise to global socio-political movements. This study explores social media's role in aiding nurses in social justice activism. The study objectives are (1) to explore how Canadian registered nurses use social media in their activism and (2) to illustrate the impact social media has on their activism. \nMethods: A qualitative, interpretive descriptive (ID) approach guided this study. I conducted eleven virtual semi-structured interviews to gather in-depth accounts of nurses’ experiences with social media activism. Ten of the interview transcripts were analyzed using an inductive ID approach to identify practical applications of the findings in nursing.\nFindings: Three significant themes were identified, illuminating how social media featured in the participants' nursing activism. The first theme, Information, Networks and Relationships, include participant accounts of how social media helps them connect with others, access relevant information, and feel empowered. Getting the Nursing Voice Out There pertains to participants' use of social media to advance the nursing voice into the public sphere and make their nursing activism and work known to a broader audience. The third theme, Opportunities for Nurse Leadership, describes the impact nurse leadership's social media presence had on participants’ activism.\nConclusion: The findings of this study highlight how nurses can strategically use social media to maximize the impact of their activism and the challenges they may encounter. The results provide insight into how informed, professional, and appropriate social media activism may increase the nursing profession’s capacity to be a force for positive social change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".