Nursing lives in the blogosphere: A thematic analysis of anonymous online nursing narratives
Bibliographic record
Abstract
AIM: The aim of this study was to explore the work-life narratives of nurses through a thematic analysis of the nursing accounts they post in their publicly accessible, anonymous blogs. BACKGROUND: Many nurses participate on social media. Blogs have been advocated as a self-reflective tool in nursing practice, yet as far as the authors are aware, no previous studies have explored nurses' individual blogs for their potential to reveal nurses' perceptions of nursing work. DESIGN: The research design was qualitative description. METHODS: Between May-August 2015, Internet search engines were used to discover lists of nursing blogs recommended by organizations representing nurses' interests. Recommended blogs were purposively sampled. Four anonymous blogs written by nurses from different nursing specialties met the sampling criteria. All 520 of their entries from 2014 were read and copied into NVivo 10, where an inductive coding process was followed. FINDINGS: Three major themes arose in these nurses' online discussions of their work lives: they truly care about and value their nursing work, but they are feeling stressed and burnt out and they are using their anonymous blogs to share factors that frustrate them in their nursing work. Three main areas of frustration were revealed: teamwork problems, challenging patients and families, and management issues. CONCLUSION: Anonymous nursing blogs offer valuable, longitudinal insights into nurses' perceptions of their work lives. Nursing blogs should be further explored for ongoing insights into nurses' experiences of nursing work, as well as nurses' recommendations for addressing issues causing them to feel frustrated in their work environments.
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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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".