Disentangling the diversity of profiles of adaptation in youth during COVID-19
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak has major psychosocial consequences on the global population and specialists report that youth may be significantly impacted. Adolescents and young adults, for whom social life is an important protective factor, had to face a new isolation caused by social distancing and home schooling. This study aims to explore youth's profiles of adaptation to COVID-19 pandemic in the province of Quebec, Canada, and the risk factors and strengths associated with each profile. METHODS: A sample of 4936 youth living in Quebec were recruited on social media and filled out an online survey during the lockdown of the first wave of COVID-19. They completed measures of psychological distress, positive adaptation (well-being, resilience), risk factors (alexithymia and emotional dysregulation), COVID-related worries and fear of contamination and COVID-related post-traumatic stress disorder (PTSD). RESULTS: The results of the latent class analysis showed four patterns of adjustment. The Resilient group (36.6% of the sample) showed the highest probability of a positive adaptation. The High distress class (29.5%) reported clinical distress, low to moderate symptoms of PTSD and fear of contamination and no significant well-being. The Moderate symptoms class (17.55%) showed moderate levels of distress and COVID-related symptoms, with half of the group still showing significant well-being. The Traumatized class (16.35%) reported the worst adaptation. Correlates significantly differentiated profiles. LIMITATIONS: The study relied on a convenience sample and a cross-sectional design. CONCLUSION: Disentangling the diversity of adaptation profiles may orient more adapted resources for youth in need during this unprecedented crisis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it