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 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 | 0.152 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".