Clinical and Vaccine Trials for COVID-19: Key Considerations from Social Science || Essais cliniques de traitements et de vaccins contre la COVID-19 :considérations clés issues des sciences sociales || Estudos Clínicos E Ensaios Com Vacinas Contra A COVID-19: Principais considerações das ciências sociais
Bibliographic record
Abstract
Previous months have witnessed a growth of clinical research in response to the COVID-19 crisis. This brief sets out social science considerations that can inform clinical and vaccine trials for COVID-19. It was developed for SSHAP by London School of Hygiene and Tropical Medicine (LSHTM) by Rose Burns, Alex Bowmer, Luisa Enria, Samantha Vanderslott (University of Oxford) and Shelley Lees in order to inform design, recruitment and community engagement, implementation and results dissemination for trials of emerging vaccine and therapeutic candidates for COVID-19. Colleagues from Dalhousie University and Kings college London reviewed it. Nos últimos meses houve um aumento da pesquisa clínica em resposta à crise da COVID-19. Esta síntese apresenta a contribuição das ciências sociais nos ensaios clínicos com vacinas contra a COVID-19. A síntese foi elaborada para a Plataforma das Ciências Sociais na Ação umanitária (Social Science in Humanitarian Action Platform - SSHAP) pela Escola de Higiene e Medicina Tropical de Londres (London School of Hygiene and Tropical Medicine - LSHTM), por Rose Burns, Alex Bowmer, Luisa Enria, Samantha Vanderslott (Universidade de Oxford) e Shelley Lees, com o objetivo de informar o desenho, recrutamento e envolvimento comunitário, a implementação e disseminação dos resultados de ensaios para novas vacinas e terapêuticas candidatas para a COVID-19. O documento foi revisado por colegas da Universidade Dalhousie e do Kings College London. A SSHAP é responsável pelo resumo.||Please note: there is an accompanying infographic summarising the key points from the briefing. Lors des mois précédents, l'on a assisté à une augmentation de la recherche clinique en réponse à la crise de la COVID-19. Cette note stratégique énonce des considérations issues des sciences sociales susceptibles d'éclairer les essais cliniques de traitements et de vaccins contre la COVID-19. Elle a été élaborée pour la SSHAP par la London School of Hygiene and Tropical Medicine (LSHTM) par Rose Burns, Alex Bowmer, Luisa Enria, Samantha Vanderslott (Université d'Oxford) et Shelley Lees afin d’éclairer la conception, le recrutement et l’engagement communautaire, la mise en oeuvre et la diffusion des résultats inhérents aux essais de nouveaux vaccins et de candidats thérapeutiques dans le cadre de la riposte contre la COVID-19.
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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 | 0.538 | 0.495 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".