Vulnerability Factors Associated with College Adjustment Trajectories During the First Wave of the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has overturned the lives of students in higher education. In this quantitative longitudinal study, we examine trajectories of college adjustment (in terms of academic and social functioning) and associated personal vulnerability factors during the first wave of the pandemic. Ten Quebec colleges invited all their newcomer students as well as those already enrolled in an adapted service to complete a questionnaire twice: in October 2019 (pre-COVID-19) and April–May 2020 (peak of the first wave). The questionnaire included college adjustment scales and questions on disability status, GPA, parental incomes, and COVID-19 experience. The final sample comprises 1,435 students (mean age = 18.2 years) of which 42% are students with a learning or affective disorder. The trajectory analysis (growth mixture models) indicates that most students perceived moderate declines in academic and social adjustment during the first wave of COVID-19. Ten percent of students reported large declines in academic and social adjustment, whereas only 4% reported improvements. Students with mental health diagnosis, lower parental income, and lower high school GPA were generally at greater risk for following a low-functioning or worsening trajectory compared to other students. We recommend preventive measures to reduce the pandemic’s long-term effects on academic and professional outcomes.
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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 | 0.001 | 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".