Using behavioural science in public health settings during the COVID-19 pandemic: The experience of public health practitioners and behavioural scientists
Bibliographic record
Abstract
INTRODUCTION: The emergence of COVID-19 and the importance of behaviour change to limit its spread created an urgent need to apply behavioural science to public health. Knowledge mobilisation, the processes whereby research leads to useful findings that are implemented to affect positive outcomes, is a goal for researchers, policy makers and practitioners alike. This study aimed to explores the experience of using behavioural science in public health during COVID-19, to discover barriers and facilitators and whether the rapidly changing context of COVID-19 influenced knowledge mobilisation. METHODS: We conducted a semi-structured interview study, with ten behavioural scientists and seven public health professionals in England, Scotland, Wales, The Netherlands and Canada. We conducted an inductive thematic analysis. RESULTS: We report three key themes and 10 sub-themes: 1.Challenges and facilitators of translation of behavioural science into public health (Methods and frameworks supported translation, Lack of supportive infrastructure, Conviction and sourcing of evidence and Embracing behavioural science) 2. The unique context of translation (Rapid change in context, the multi-disciplinary team and the emotional toll). 3. Recommendations to support future behavioural science translation (Embedding experts into teams, Importance of a collaborative network and showcasing the role of behavioural science). DISCUSSION: Barriers and facilitators included factors related to relationships between people, such as networks and teams; the expertise of individual people; and those related to materials, such as the use of frameworks and an overwhelming amount of evidence and literature. CONCLUSION: People and frameworks were seen as important in facilitating behavioural science in practice. Future research could explore how different frameworks are used. We recommend a stepped competency framework for behavioural science in public health and more focus on nurturing networks to facilitate knowledge mobilisation in future emergencies.
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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 | 0.119 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.045 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".