#COVID19 and #Breastcancer: A Qualitative Analysis of Tweets
Bibliographic record
Abstract
Rapid and efficient communication regarding quickly evolving medical information was paramount for healthcare providers and patients throughout the COVID-19 pandemic. Over the last several years, social media platforms such as Twitter have emerged as important tools for health promotion, virtual learning among healthcare providers, and patient support. We conducted a qualitative thematic content analysis on tweets using the hashtags #BreastSurgery, #BreastCancer, #BreastOncology, #Pandemic, and #COVID19. Advocacy organizations were the most frequent authors of tweets captured in this dataset, and most tweets came from the United States of America (64%). Seventy-three codes were generated from the data, and, through iterative, inductive analysis, three major themes were developed: patient hesitancy and vulnerability, increased efforts in knowledge sharing, and evolving best practices. We found that Twitter was an effective way to share evolving best practices, education, and collective experiences among key stakeholders. As Twitter is increasingly used as a tool for health promotion and knowledge translation, a better understanding of how key stakeholders engage with healthcare-related topics on the platform can help optimize the use of this powerful tool.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".