Content analysis of behaviour change techniques in government physical distancing communications for the reopening of schools during the COVID-19 pandemic in Ireland
Bibliographic record
Abstract
Background: Effective government communications and leadership are central to the management of pandemics. Behavioural science can offer important insight into the development of such communications strategies. The extent to which established behaviour-change science is reflected in current government messaging campaigns to promote adherence to physical distancing measures in the context of the coronavirus disease 2019 (COVID-19) pandemic is unclear. The current study aimed to describe the behaviour-change content of a set of government-issued poster communications for the reopening of schools in Ireland during the COVID-19 pandemic in September 2020. Methods: Posters targeting physical distancing behaviours in school settings were retrieved from the Government of Ireland website for analysis. Posters were independently coded for behaviour change techniques (BCTs) using the BCT Taxonomy Version 1, a hierarchically clustered taxonomy of 93 distinct BCTs across 16 groups. The Theories and Techniques tool was used to identify mechanisms of action (MoAs) linked to each of the identified BCTs. Eight posters were independently content-analysed by two members of the research team for BCTs and linked MoAs. Results: Eight unique BCTs from six unique groups were identified in at least one poster. These BCTs were linked with 11 unique MoAs through which behaviour change is theorised to occur. Several theoretically important groups of BCTs, such as Natural Consequences, Social Support, Shaping Knowledge, and Comparison of Behaviour, were underutilised or not included in any of the posters. Conclusion: Future poster communications could benefit from including additional BCTs from key groups, particularly Natural Consequences. This article provides proof-of-concept evidence for future evaluations of government public health communications for behaviour-change content using existing taxonomies and tools.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".