ArticleBehavioral sciences (Basel, Switzerland)2024
COVID-19 Personal Protective Behaviors during Large Social Events: The Value of Behavioral Observations.
Article in Behavioral sciences (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.
- Preventative behavioural interventions that reduce health inequities: a systematic review using the theoretical domains framework.BMC public health · 2025Pooled it
- Mission, activities, and ways of working at the Behavioural Science Unit of Public Health Wales.Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025Review
- Promoting Behavioral Change to Improve Health Outcomes.Behavioral sciences (Basel, Switzerland) · 2025Article
- Integrating Systems Thinking and Behavioural Science.Behavioral sciences (Basel, Switzerland) · 2025Review
- Closing the Gap: How Psychological Distance Influences Willingness to Engage in Risky COVID Behavior.Behavioral sciences (Basel, Switzerland) · 2024Article
- Decision making techniques in mass gathering medicine during the COVID-19 pandemia: a scoping review.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Within the context of reopening society in the summer of 2021, as the UK moved away from 'lockdowns', the Government of Wales piloted the return of organized 'mass gatherings' of people at a number of test events. The current study reports behavioral observations that were made at two of the test events to inform this process. The researchers were particularly interested in four key factors: how (1) context within a venue, (2) environmental design, (3) staffing and social norms, and (4) time across an event, affected the personal protective behaviors of social distancing and face-covering use. Data collection was undertaken by trained observers. Adherence to protective behaviors was generally high, but there is clear evidence that these behaviors were shaped in a systematic way by the environment, situational cues, and the passage of time during the events. Some instances of large-scale non-adherence to personal protective behaviors were documented. An analysis within a dual-process framework suggests ways to understand and respond to supporting target health behaviors in groups of people where intervention is deemed valuable, such as in complex or ambiguous contexts. This is one of the first studies to include a 'true' behavioral measure in understanding human responses to COVID-19. It demonstrates that behavioral observations can add precision and granularity to understanding human behavior in complex real-world contexts. Given the significant physical and mental health burden created acutely and chronically by COVID-19, this work has implications for how governments and organizations support target populations in other complex challenges facing us today, such as in sustainability, and healthy lifestyle behaviors. An individual's intentions are not always matched by their actions, and so the findings support a balanced liberal paternalistic approach where system-level changes support appropriate individual-level decisions to engender collective responsibility and action.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.