COVID-19 related information seeking: The impact of media on parental concerns
Bibliographic record
Abstract
The expansion of information sources and their use has accelerated since the beginning of the COVID-19 pandemic, sometimes provoking significant concern in the daily lives of parents. The objective of this study was to investigate the association between COVID-19 related information sources and the level of concern about COVID-19 among parents of school-aged children. Using factor analysis and hierarchical ascending classification, we constructed groups according to the information sources they used. We performed ANOVA analysis and then binomial logistic regression to compare concern levels among the groups created. Overall, the 3,459 participants were mainly women (79.2%) and 59.5% reported being between 35 and 44 years old. The mean concern score in our sample was 9.5/15 (s.d. = 3.87). The whole sample fell into three groups: (1) Traditional Media (n = 1,610), who mainly used newspapers; (2) Online Social Networks and Entourage (n = 776), who mostly consulted online social media as well as friends and family; and (3) the Unplugged (n = 1,073), who consulted few or no information sources. Compared to the Unplugged, individuals in the other two groups had a higher risk of being concerned (Traditional Media, OR = 2.2; p < 0.001; Social Networks and Entourage, OR = 3.1; p < 0.001). Communication about pandemic risk should be conveyed based on reliable information and at moderate intervals to safeguard the mental health of individuals.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".