Identifying potential factors associated with PCR testing for COVID-19 among Australian young people: cross-sectional findings from a longitudinal study
Bibliographic record
Abstract
BACKGROUND: Testing has played a crucial role in reducing the spread of COVID-19. Though COVID-19 symptoms tend to be less severe in adolescents and young adults, their highly social lifestyles can lead to increased transmission of the virus. In this study, we aimed to provide population-based estimates of polymerase chain reaction testing (PCR) for the COVID-19 pandemic and identify factors associated with PCR testing in Australian youth using the latest survey data from the Longitudinal Study of Australian Children (LSAC). METHODS: We used the latest wave (9C1) of the LSAC, collected from 16 to 21-year-old Australians via an online survey between October and December 2020. In total, 2291 youths responded to the questions about COVID-19 testing including factors related to the coronavirus restriction period (CRP) in Australia. Both bivariate and multivariate logistic regression analyses were performed to identify variables (sociodemographic factors and factors related to CRP) associated with COVID-19 testing. RESULTS: During the study period, 26% (n = 587) of Australian youth aged between 16 and 21 years were tested for COVID-19. The strongest predictor of COVID-19 testing was living in major cities (aOR 1.82, 95% CI:1.34-2.45; p < 0.01). Increased age (aOR 1.97, 1.00-3.89; p < 0.05) and having a pre-existing medical condition (aOR 1.27, 1.02-1.59; p < 0.05) were also significantly associated with a higher likelihood of COVID-19 testing. CONCLUSION: Age, remoteness and having a pre-existing medical illness were associated with PCR COVID-19 testing among Australian youth aged between 16 and 21 years in the first year of the COVID-19 pandemic. More research is warranted to identify factors associated with other COVID-19 testing methods and address the specific barriers that may limit COVID-19 testing in this age group.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".