Apparatuses of Knowledge Delivery to Patients: The Role of Social Media in Vaccine Controversies.
Bibliographic record
Abstract
The practice of knowledge delivery to patients has long been performed by healthcare professionals, who were seen as trustable sources of healthcare knowledge. However, healthcare knowledge is now being distributed widely online and in particular on social media, by numerous individuals who are sharing a mixture of scientific/non-scientific information grounded in personal perspectives and experiences. In the shift to healthcare knowledge delivery on social media, traditional practices of knowledge delivery to patients are challenged. This study draws on material-discursive practices, known as apparatuses, to examine two notable material-discursive practices in vaccine administration. This research is expected to make two contributions to the IS literature. First, it aims to identify significant differences in the two knowledge delivery practices and their outcomes. Second, it aims to investigate the ongoing interaction and tension between traditional and new knowledge delivery approaches. We provide preliminary insights and a roadmap for further developing this research.
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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 | 0.044 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".