Navigating Emerging Adulthood: Exploring Current Challenges Experienced in the Community
Bibliographic record
Abstract
Background: Despite experiencing challenges, many young people resist seeking formal mental health support services (McGorry & Mei, 2018). One possible reason for this underutilization is that outreach and current service frameworks might not meet the evolving needs of this group, especially among young people with marginalized identities (Robards et al., 2018). In collaboration with Hôtel-Dieu Grace Healthcare, a lead agency engaged with local community mental health services, this project seeks to contribute to initiatives specific to transitional-aged youth's mental health, possible trauma symptoms, and help-seeking.Method: This study invites undergraduate students and transitional-aged youth (18-24 years old) residing in Windsor-Essex County, Ontario, to participate in a mixed-method project with two phases. The first phase is ongoing and consists of a quantitative online survey. The next phase of this project will involve adopting a narrative inquiry approach and interviewing a subsample of participants from community and university settings.Results: Preliminary analyses will be presented. Descriptive and correlational findings will reveal how local transitional-aged youth are coping with mental health challenges and engaging in help-seeking behaviours within the context of the COVID-19 pandemic and related restrictions and disruptions.Conclusion: The expected implications of this mixed-method study include gaining valuable insights into understanding the unique challenges experienced by a traditionally hard-to-reach population during the COVID-19 pandemic. The resulting insights may be leveraged to inform and refine existing support services. This project aims to accomplish these objectives by conceptualizing the problem at the local level from the youth perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".