An Information-Motivation-Behavioral Skills (IMB) model of pandemic risk and prevention
Bibliographic record
Abstract
COVID-19 will be with us well into the future, and four years into the pandemic, it continues to cause serious individual and public health consequences and economic impact worldwide. Mindful of the staggering continuing costs of the COVID-19 pandemic, calls are urgently being made to “prepare now for the next pandemic.” Containing future pandemics will require at the very core widespread, voluntary, and sustained behavior change to prevent spread of pandemic disease. Such efforts must be based upon well-validated behavioral science models of health behavior change articulated to foreseeable future pandemic contexts. We present an Information-Motivation-Behavioral Skills (IMB) Model of Pandemic Risk and Prevention as a conceptual foundation for understanding the determinants and dynamics of pandemic risk and preventive behavior and as a systematic framework for the design, implementation, and evaluation of interventions to promote and maintain pandemic preventive behavior. Our model is highly generalizable across pandemic scenarios. It is currently testable in the context of COVID-19, and can be tested in future localized epidemics and in pandemic simulation studies. The IMB model of Health Behavior Change, upon which our new model is based, is an empirically well validated and supported multivariate model utilized successfully for decades to understand and promote behavior change in multiple health domains. Our introduction of the IMB Model of Pandemic Risk and Prevention aims to contribute to theoretically- and empirically-based efforts to reduce risk and promote prevention in future pandemics and in the continuing COVID-19 pandemic.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".