The self-selected use of social media for the pre-registration student nurse journey: An interpretative phenomenological analysis
Bibliographic record
Abstract
Objective: To explore the lived experience of the phenomenon of self-selected social media use through the viewpoint of UK pre-registered student nurses in relation to their studies.Methods: Seven UK pre-registration student nurses who used social media in relation to their nursing studies were interviewed during February and March 2020. Semi-structured interviews were transcribed and analysed using interpretative phenomenological analysis.Results: Four themes indicated that students used social media to discover, create and control their ‘own space’ for personal and professional benefit, through a ‘whole new world’ of social connections, ‘opening doors’ for learning and development to support themselves and each other on their ‘journey to be nurses’. The analysis also revealed an inherent journey of self-discovery affording self-empowerment and resilience, the significance of which was dependent on each participant’s characteristics and experiences.Conclusions: Social media use may have the potential to assist student nurse decisions related to, enhance the experience of, and engagement in, their education. As an international phenomenon, social media could be considered as an agent to improve student nurse retention and facilitate recruitment. Future research is recommended to explore the use of social media in this way as well as the challenges to its use.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".