Social media opposition to the 2022/2023 UK nurse strikes
Bibliographic record
Abstract
Previous research has established that the success of strikes, and social movements more broadly, depends on their ability to garner support from the public. However, there is scant published research investigating the response of the public to strike action by healthcare workers. In this study, we address this gap through a study of public responses to UK nursing strikes in 2022-2023, using a data set drawn from Twitter of more than 2300 publicly available tweets. We focus on negative tweets, investigating which societal discourses social media users draw on to oppose strike action by nurses. Using a combination of corpus-based approaches and discourse analysis, we identified five categories of opposition: (i) discourse discrediting nurses; (ii) discourse discrediting strikes by nurses; (iii) discourse on the National Health System; (iv) discourse about the fairness of strikers' demands and (v) discourse about potential harmful impact. Our findings show how social media users operationalise wider societal discourses about the nursing profession (e.g., associations with care, gender, vocation and sacrifice) as well as recent crises such as the Covid-19 pandemic to justify their opposition. The results also provide valuable insights into misconceptions about nursing, strike action and patient harm, which can inform strategies for public communication.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".