Post pandemic research priorities: A consensus statement from the HL-PIVOT
Bibliographic record
Abstract
We have been amid unhealthy living and related chronic disease pandemics for several decades. These longstanding crises have troublingly synergized with the coronavirus disease 2019 (COVID-19) pandemic. The need to establish research priorities in response to COVID-19 can be used to address broad health and wellbeing, social and economic impacts for the future is emerging. Accordingly, this paper sets out a series of research priorities that could inform interdisciplinary collaboration between clinical sciences, public health, business, technology, economics, healthcare providers, and the exercise science/sports medicine communities, among others. A five-step methodology was used to generate and evaluate the research priorities with a focus on broad health and well-being impacts. The methodology was deployed by an international and interdisciplinary team from the Healthy Living for Pandemic Event Protection (HL- PIVOT) network. This team were all engaged in responding to the Pandemic either on the 'front-line' and/or in leadership positions ensuring the currency and authenticity of the process. Eight research priorities were identified clustered into two groups: i) Societal & Environmental, and ii) Clinical. Our eight research priorities are presented with insight from previously published research priorities from other groups.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".