A multiple behaviour temporal network analysis for health behaviours during COVID-19
Bibliographic record
Abstract
Background: The aim of this study was to examine the temporal dynamics of health behaviours (e.g., physical activity, alcohol consumption) and pandemic related health behaviours (e.g., hand washing, physical distancing) using network psychometrics. Methods: This hypothesis-generating analysis used temporal network models to fit temporal net-works, contemporaneous networks, and between-subject networks from items within the International COVID-19 Awareness and Responses Evaluation (iCARE) survey. The iCARE study is a multi-wave observational cohort study of public awareness, attitudes, and responses to public health policies implemented to reduce the spread of COVID-19 on people around the world. Results: Descriptive statistics revealed that over six months, adherence to mask wearing, social distancing, hand washing, physical activity, and alcohol consumption remained generally stable. People reported a decrease in healthy diet before this be-haviour returned to pre-pandemic levels. Between February and May respondents to the iCARE survey also reported smoking cigarettes, using recreational drugs, and vaping ‘a lot more’ since the start of the pandemic; however, this pattern reversed abruptly from May to July with most participants reporting they engage in these behaviours ‘a lot less’ than be-fore the pandemic or ‘not at all’. Temporal associations were observed between physical activity and health eating, and a bi-directional relationship was evident between outdoor mask use and vaping. A contemporaneous network revealed associations between vice behaviours (drugs, vaping, cigarettes, and alcohol), and within person associations between drug use, physical activity, and healthy eating. Conclusions: The application of temporal network analysis to the study of multiple health behaviours is well suited to address key re-search questions in the field such as ‘how to multiple health behaviours co-vary with one another over time’. Future research employing intensive time series data and measuring affective and cognitive mediators of behaviour, in addition to health behaviours, has the potential to contribute valuable hypothesis generating insights to the basic science of multiple health behaviour research.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".