A scoping review of research on the determinants of adherence to social distancing measures during the COVID-19 pandemic
Bibliographic record
Abstract
This scoping review focused on answering key questions about the focus, quality and generalisability of the quantitative evidence on the determinants of adherence to social distancing measures in research during the first wave of COVID-19. The review included 84 studies. The majority of included studies were conducted in Western Europe and the USA. Many lacked theoretical input, were at risk for bias, and few were experimental in design. The most commonly coded domains of the TDF in the included studies were 'Environmental Context and Resources' (388 codes across 76 studies), 'Beliefs about Consequences' (34 codes across 21 studies), 'Emotion' (28 codes across 12 studies), and 'Social Influences' (26 codes across 16 studies). The least frequently coded TDF domains included 'Optimism' (not coded), 'Intentions' (coded once), 'Goals' (2 codes across 2 studies), 'Reinforcement' (3 codes across 2 studies), and 'Behavioural Regulation' (3 codes across 3 studies). Examining the focus of the included studies identified a lack of studies on potentially important determinants of adherence such as reinforcement, goal setting and self-monitoring. The quality of the included studies was variable and their generalisablity was threatened by their reliance on convenience samples.
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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 | 0.019 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".