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Record W4399593610 · doi:10.2196/54288

Measuring Population-Level Adolescent Mental Health Using a Single-Item Indicator of Experiences of Sadness and Hopelessness: Cross-Sectional Study

2024· article· en· W4399593610 on OpenAlexvenueno aff
Jorge Verlenden, Sanjana Pampati, Melissa Heim Viox, Nancy D. Brener, Laima Licitis, Patricia Dittus, Kathleen A. Ethier

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersOak Ridge Institute for Science and EducationNutrition Obesity Research Center, University of North CarolinaCenters for Disease Control and PreventionU.S. Department of Energy
KeywordsSadnessCross-sectional studyPsychologyMental healthClinical psychologyPopulationPsychiatryMedicineEnvironmental healthAnger

Abstract

fetched live from OpenAlex

BACKGROUND: Population-level monitoring of adolescent mental health is a critical public health activity used to help define local, state, and federal priorities. The Youth Risk Behavior Surveillance System includes a single-item measure of experiences of sadness or hopelessness as an indicator of risk to mental health. In 2021, 42% of high school students reported having felt sad or hopeless for 2 weeks or more during the past 12 months. The high prevalence of US high school students with this experience has been highlighted in recent studies and media reports. OBJECTIVE: This study seeks to examine associations between this single-item measure of experiences of sadness or hopelessness with other indicators of poor mental health including frequent mental distress and depressive symptoms. METHODS: We analyzed survey data from a national sample of 737 adolescents aged 15-19 years as a part of the Teen and Parent Surveys of Health. Participants were recruited from AmeriSpeak, a probability-based panel designed to be representative of the US household population. Feeling sad or hopeless was operationalized as a "yes" response to the item, "During the past 12 months, did you ever feel so sad or hopeless almost every day for 2 weeks or more in a row that you stopped doing some usual activities?" Unadjusted and adjusted prevalence ratios (aPRs) were calculated to examine associations between the single-item measure of having felt sad or hopeless almost every day for 2 weeks with moderate to severe depressive symptoms, frequent mental distress, and functional limitation due to poor mental health. Adjusted models controlled for age, race and ethnicity, sex assigned at birth, and sexual identity. RESULTS: Overall, 17.3% (unweighted: 138/735) of adolescents reported that they felt sad or hopeless for 2 weeks or more during the past 12 months, 30.2% (unweighted: 204/716) reported moderate to severe depressive symptoms, 18.4% (unweighted: 126/732) reported frequent mental distress, and 15.4% (unweighted: 107/735) reported functional limitation due to poor mental health. After adjusting for demographics, adolescents who reported that they felt sad or hopeless for 2 weeks or more were 3.3 times as likely to report moderate to severe depressive symptoms (aPR 3.28, 95% CI 2.39-4.50), 4.8 times as likely to indicate frequent mental distress (aPR 4.75, 95% CI 2.92-7.74), and 7.8 times as likely to indicate mental health usually or always interfered with their ability to do things (aPR 7.78, 95% CI 4.88-12.41). CONCLUSIONS: Associations between having felt sad or hopeless for 2 weeks or more and moderate to severe depressive symptoms, frequent mental distress, and functional limitation due to poor mental health suggest the single-item indicator may represent relevant symptoms associated with poor mental health and be associated with unmet health needs. Findings suggest the single-item indicator provides a population-level snapshot of adolescent experiences of 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.225
GPT teacher head0.468
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2024
Admission routes1
Has abstractyes

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