Changes in Rates of Special Considerations in Higher Education Applications Pre‐ and During the <scp>COVID</scp>‐19 Pandemic in Victoria, Australia
Bibliographic record
Abstract
BACKGROUND AND AIMS: Since the onset of the COVID-19 pandemic, a significant rise in mental ill health has been observed globally in young people, particularly those in their final years of secondary school. Students' negative experiences coincide with a critical transitional period which can disrupt milestones in social and educational development. This study aimed to use innovative population-level data to map the impact of the pandemic on students entering higher education. METHODS: Pre-pandemic (2019/2020) and pandemic (2020/2021) tertiary education application data were obtained from the Victorian Tertiary Admissions Centre. Prevalence of applications for special consideration related to mental ill health were compared between cohorts across various geographical areas and applicant demographic subgroups. Relative risk regression models were used to understand the role of different risk factors. RESULTS: Rates of mental health-related special consideration applications increased by 38% among all applications (pre-pandemic: 7.8%, n = 56 916; pandemic: 10.8%, n = 58 260). Highest increases were observed among students in areas with both extended and close-quarter lockdown experiences, and areas impacted by 2019/2020 black summer bushfires. The increases were higher among Year 12 students and students with other special consideration needs (e.g., physical condition, learning disability). Slightly higher increases were observed in areas with higher socio-economic status, which may potentially be related to inequality in mental health service access. CONCLUSION: As consequences of mental health difficulties and academic disruption in youth can be long lasting, it is critical to establish a mental health support framework both in and outside of higher education to facilitate young people's recovery from the pandemic.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".