Bibliographic record
Abstract
COVID‐19, nutrition and mental health emerged as the most mentioned trends among the health and wellness influencer discussions on Twitter during the third quarter of 2020, GlobalData, a leading data and analytics company, announced Nov. 27. The discussions related to COVID‐19 were largely driven by how the virus can be crushed with simple changes in lifestyle until the vaccines arrive, majorly focusing on metabolic health, as patients with metabolic syndrome are at higher risk of infection. Nutrition emerged as another most mentioned trend, led by a surge in discussions related to the nutritious diet tips to boost immunity shared by leading health and wellness experts. It was followed by mental health, as the mental well‐being of people, including children, has been hampered by the COVID‐19 pandemic. The negative consequences of school closures have had a profound impact on children's mental health. The Centers for Disease Control and Prevention has emerged as the most mentioned organization among the health and wellness influencer discussions during the quarter, followed by the University of Toronto and the Centre for Addiction and Mental Health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".