2020 International Behavioural Trials Network (IBTN) Conference Abstracts
Bibliographic record
Abstract
To optimize the uptake and impact of behavioural interventions in the context of non-communicable chronic diseases (NCD) prevention and treatment, we need more quantity and better quality of evidence.To accomplish this, we need to cultivate a culture among behavioural trialists that espouses the rigorous development and testing of carefully designed, well-defined behavioural interventions that are seen as relevant and implementable by healthcare systems and third party payers.To advance this agenda, a group of international investigators, led by Drs.Simon Bacon, Kim Lavoie, and Gregory Ninot, created the International Behavioural Trials Network (IBTN), which currently has 842 members from six continents.The mission of the IBTN (www.ibtnetwork.org) is to foster global improvement in the quality of behavioural trials and in trial implementation, and to share a repository for existing recommendations, tools, and methodology on behavioural trials and intervention development.The goal is to develop a more solid and widely accepted evidence base for the transferability of behavioural interventions into disease prevention, health promotion and intervention practice and policy.The members of the IBTN met for their 3rd international conference on May 28-29, 2020, based in Montreal, Canada but held virtually.The meeting had 743 registrants that included researchers, clinicians, public health and implementation specialists, trainees and other stakeholders from 43 countries, and featured 25 presentations with a total of 44 speakers.The 36 peer-reviewed abstracts that were presented via virtual poster sessions are presented below.
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 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 | 0.132 | 0.150 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.376 | 0.257 |
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".