Bibliographic record
Abstract
Psychology of Behavioural Interventions and Pandemic Control is a unique text that examines the COVID-19 pandemic in relation to population risk factors and the efficacy of non-pharmaceutical interventions deployed by many governments around the world to bring the pandemic under control. The book presents critical and insightful lessons that can be drawn up to assess governments’ performance in relation to the pandemic and to guide the construction of effective measures to put in place in readiness for any future public health crises on this scale. It starts by examining lessons learned from historical pandemics and then turns to early epidemiological modelling that influenced the decision of many governments to implement wide-ranging interventions designed to bring public behaviour under close control. It also examines the findings of research that tried to understand pre-existing population risks factors which had some mediating influences over COVID-19, mortality rates, and the effects of interventions. Early modelling work is critiqued, and the discussion also identifies weaknesses in early modelling research. The author, Barrie Gunter, goes on to consider ways in which multiple disciplines can be triangulated to produce more comprehensive models of risk. He also offers suggestions on how future pandemic-related research might be constructed to deliver more powerful analyses of the effects of interventions and the role played by different population risk factors. This insight might then deliver better policies for pandemic control and for safe release from that control. This is essential reading for students and researchers in psychology, public health and medical sciences. It would also be of interest to policy makers assessing government strategies, responses and performance.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".