Use of mind genomics for public health and wellbeing: Lessons from COVID 19 pandemic
Bibliographic record
Abstract
ABSTRACT Background: Machine learning (ML) tools can be used to analyze human mindsets and forecast behavioral patterns. ML can be used to understand the psychological processes and behavioral principles underlying public decision-making patterns. The aim of this study was to explore participants’ mindsets using ML and accordingly build messages for each mindset to enhance compliance with a public health policy, specifically physical distancing during the coronavirus disease 2019 (COVID-19) pandemic. Methods: An online questionnaire was administered using systematically varied combinations of elements and science of mind genomics. The questions focused on the perceived risk level of COVID-19, strategies to enhance physical distancing compliance, appropriate communicators of the policy, and different physical distancing practices. Snowball sampling was used to recruit participants until sample saturation was achieved among residents of the United Arab Emirates (UAE), aged 18– 80 years. Results: A total of 117 patients were included in this study. In the total panel, the strongest performing elements were those communicated by the government ( P <0.01) and clergy ( P < 0.05), with no differences between sex and age groups. Three mindset segments were identified: (1) followers of general strategies for physical distancing, (2) those interested in novel ways of practicing physical distancing, and (3) fascinating onlookers of the pandemic. Conclusion: Our results revealed that COVID-19 health-related messages are best communicated by the government and clergy in the UAE. These strategies may aid in the implementation and adoption of other public health policies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".