Predictors of COVID testing among Australian youth: Insights from the Longitudinal Study of Australian Children
Bibliographic record
Abstract
ABSTRACT Background Testing has played a crucial role in reducing the spread of COVID. Although COVID symptoms tend to be less severe in children and adolescents, a key concern is young people’s role in the transmission of the virus given their highly social lifestyles. In this study, we aimed to identify the predictors associated with COVID testing in Australian youth using data from the Longitudinal Study of Australian Children (LSAC). Methods We used the latest wave 9C1 of the LSAC, where data were collected from 16–21-year-old Australians via an online survey between October and December 2021. In total, 2291 Australian youths responded to the questions about COVID testing and COVID symptom severity. Data was stratified by living with/without parents, and bivariate and logistic regression analyses examined predictor variables (age, sex, country of birth, remoteness, education level, employment, relationship status, number of household members, living with parents, receiving the COVID financial supplement from government and index of relative socio-economic advantage and disadvantage) and their distributions over the outcome variable COVID testing. Results Youths aged 16-17 were more likely to live at home than youths aged 20-21 years. The strongest predictor of COVID testing was living in major cities (regardless of living with or without parents). Changed household composition was significantly associated with COVID testing among the youths living in the parental home. While among the respondents living without their parents, living with multiple household members and low or no cohesion among household members was associated with higher rates of COVID testing. Conclusion Our study revealed young people have been very good at getting tested for COVID. To further incentivise testing in this age group, we should consider providing this age group with continued financial and social support while awaiting the outcome of the test and during any isolation. Strengths and limitations of this study Large national cohort of young people strengthened the findings of the study and allowing us to examine the factors associated with COVID testing for the first time in Australia. A broad-based assessment of potential predictors of COVID testing, including sociodemographic and coronavirus specific factor. Cross-sectional observational design limits causal inference. Self-reported information about COVID testing can be subject to recall as well as social desirability bias.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".