“What Are Some of the Things You Are Worried About?”: An Analysis of Youth's Open‐Ended Responses of Current Worries
Bibliographic record
Abstract
INTRODUCTION: There is widespread concern that contemporary global issues (e.g., climate change, technology use) are exacerbating a "youth wellbeing crisis." However, we have heard little about this issue from youth themselves. To ascertain whether youth themselves are worried about global issues, their mental health, or other aspects of their life, we asked youth an open-ended question about their current worries. Further, we assessed whether mental health was related to self-generated worries. METHODS: = 15.60, SD = 1.65, 48.2% female) from Canada, responded to the question: "What are some of the things you are worried about?" Youth also self-reported on demographics, social anxiety, depressive symptoms, and general worry. RESULTS: Youth generated a range of worries, with the most common worries being school and their future. Few adolescents directly mentioned the state of the world, covid, or their own mental health. Worries were differentially associated with mental health problems and youth who reported worrying about "everything" or reported many worries had worse mental health compared to peers. CONCLUSIONS: Contemporary issues, that often are cited as a concern, were not a focal point of youth's responses. Asking open-ended questions to youth about their worries may be a way to identify which youth may be experiencing poor mental health.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".