Transitioning to University with a Mental Illness: Experiences of Youth and their Parent
Bibliographic record
Abstract
Despite extant research suggesting the important role of family in supporting youth with disabilities (e.g., learning disabilities) making the transition to university, family relational influences on youth with mental illness undergoing this transition remains unclear. Adopting a family resilience perspective, this mixed-methods study aimed to examine (a) how parent-child relationship factors relate to youths’ mental illness symptoms and well-being and (b) youth and parent perspectives on how parents can best support youth in this transition. A total of 225 youth with mental illness (aged 17–23, M = 18.43, SD = 0.91, 87% white) completed questionnaires assessing parent-child relationship satisfaction, depressive and anxiety symptoms, and life satisfaction. For 22 of these youth, a parent (aged 45–57, M = 49.77, SD = 3.57, 100% white) completed questionnaires assessing caregiver burden and reward. Parents (and their child) completed written responses addressing what youth most need from parents during this transition. Parent-child relationship factors were moderately associated with youth mental illness and well-being. Thematic analysis indicated much agreement and some difference between youth and caregivers on the aspects of parental support most valued during this transition. Implications for supporting such youth as they adapt to university are discussed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".