“#LongCOVID affects children too”: A Twitter analysis of healthcare workers’ sentiment and discourse about Long COVID in children and young people in the UK
Bibliographic record
Abstract
ABSTRACT Aims With a social media analysis of the discourse surrounding the prevalence of Long COVID in children and young people (CYP), this study aims to explore healthcare workers’ perceptions concerning Long COVID in CYP in the UK between January 2021 and January 2022. This will allow to contribute to the emerging knowledge on Long COVID and identify critical areas and future directions for researchers and policymakers. Design A mixed methods approach with a discourse, keywords, sentiment, and image analysis, using Pulsar™ and Infranodus. Setting A discussion of the experience of Long COVID in CYP in the UK shared on Twitter between 1 January 2021 and 31 January 2022. Participants A sample of health workers with Twitter accounts whose bio has them identifying themselves as HCWs. Results We obtained 2588 tweets. HCW were responsive to announcements issued by authorities regarding the management of COVID-19 in the UK. The most frequent feelings were negative. The main themes were uncertainty about the future, policies and regulations, managing and addressing COVID-19 and Long COVID in CYP, vaccination, using Twitter to share scientific literature and management strategies, and clinical and personal experiences. Conclusions The perceptions described on Twitter by HCW concerning the presence of Long COVID in CYP appear to be a relevant and timely issue and responsive to the declarations and guidelines issued by health authorities over time. We recommend further support and training strategies for health workers and school staff regarding the manifestations and treatment of Long COVID in CYP. Strengths and limitations of this study – Our online analysis of Long COVID contributes towards an emerging understanding of reported experiential, emotional and practical dimensions of Long COVID in CYP specifically, as well as questions of vaccine hesitancy in CYP with Long COVID. – We identify key policy areas that need considered attention and focus, such as: a) the provision of psychosocial support with access to quality mental health resources to alleviate the impact that Long COVID can have on the mental health of CYP; and b) the development of clear Long COVID pandemic recovery policies that are informed from a health equity perspective and how this affects CYP living with Long COVID. – This is one of few studies to collect healthcare workers’ perceptions regarding Long COVID in CYP in the UK, using information from Twitter. – This study is limited to the perception of those who identified as healthcare workers via their online biographies, and so is not representative of the general UK or the global population.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".