Disentangling the diversity of profiles of adaptation in youth during COVID-19
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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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".