MétaCan
Menu
Back to cohort
Record W4406604847 · doi:10.1002/jad.12470

“What Are Some of the Things You Are Worried About?”: An Analysis of Youth's Open‐Ended Responses of Current Worries

2025· article· en· W4406604847 on OpenAlexaffabout
Taylor Heffer, Meghan E. Borg, Teena Willoughby

Bibliographic record

VenueJournal of Adolescence · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsBrock UniversityOntario Tech University
Fundersnot available
KeywordsMental healthWorryPsychologyAnxietyPositive Youth DevelopmentDemographicsDevelopmental psychologyPsychiatrySociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.436
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of AdolescenceSame topicPsychological and Temporal Perspectives ResearchFrench-language works237,207