Portrayals of the new graduate nurse experience on TikTok: a thematic analysis
Bibliographic record
Abstract
• NGNs made videos saying they felt unprepared for to be nurses. The themes in NGN videos suggest that they did not understand the complexity of nursing practice. • NGNs equated having the correct clothing, equipment, and other visible markers as making them prepared for practice. • Experienced RNs can consider that NGNs may have different priorities about nursing practice. Knowledge of these differences can help RNs become better clinical mentors as they support NGNs to manage complexity. New graduate nurses (NGNs) enter the workforce with enthusiasm, but negative experiences within the first two years of practice can lead to attrition. Many NGNs are sharing their practice experiences on the popular platform, TikTok. The purpose of our study was to explore how NGNs display their experiences of transitioning to practice on TikTok. We used Braun and Clarkes’ reflexive thematic analysis to analyze 126 videos, collected from the hashtags #newgradnurse, #newgradnursetips, and #newnurseproblems on TikTok between October and November 2023. We generated three themes from these data: The NGN as an Individual, The NGN as a Clinical Nurse, and The NGN as a Professional. The results of our study demonstrated a gradual progression in the NGNs’ comfort within their transition. As NGNs entered practice, many did not demonstrate understanding about the complexities of the nursing profession. As a result, majority of NGNs reported feeling unprepared for practice. NGNs and RNs discussed different topics on TikTok, demonstrating different understandings of the roles of nurses.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".