Exploring barriers and facilitators of physical distancing in the context of the COVID-19 pandemic: a qualitative interview study
Bibliographic record
Abstract
Background: Physical distancing measures (e.g., keeping a distance of two metres from others, avoiding crowded areas, and reducing the number of close physical contacts) continue to be among the most important preventative measures used to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). Therefore, it is important to understand barriers and facilitators of physical distancing to help inform future public health campaigns. Methods: The current study aimed to qualitatively explore barriers and facilitators of physical distancing in the context of the COVID-19 pandemic using a qualitative interpretative design. Semi-structured one-to-one phone interviews were conducted with 25 participants aged 18+ years and living in the Republic of Ireland between September and October 2020. A purposive sampling strategy was used to maximise diversity in terms of age, gender, and socioeconomic status. Interviews were analysed using inductive thematic analysis. Results: Analysis resulted in the development of six main themes related to barriers and facilitators of physical distancing: (1) Maintaining and negotiating close relationships; (2) Public environments support or discourage physical distancing; (3) Habituation to threat; (4) Taking risks to protect well-being; (5) Personal responsibility to control the “controllables”; and (6) Confusion and uncertainty around government guidelines. Conclusions: Physical distancing measures were judged to be more or less difficult based on a number of internal and external psychosocial factors. Barriers to distancing included difficulties maintaining and negotiating close relationships, habituation to COVID-19-related threat, risk compensation, and confusion and uncertainty around government guidelines. Having a sense of personal responsibility to prevent COVID-19 transmission through distancing was an important facilitator. The structure of public environments was viewed as both barrier and facilitator. Barriers and facilitators may vary depending on context and life stage, which should be considered in the design of interventions to target physical distancing behaviour.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".