MétaCan
Menu
Back to cohort
Record W4307810432 · doi:10.1177/0044118x221129642

Youth Mental Health Help-Seeking Information Needs and Experiences: A Thematic Analysis of Reddit Posts

2022· article· en· W4307810432 on OpenAlexafffund
Meghan Sit, Sarah A. Elliott, Kelsey S Wright, Shannon D. Scott, Lisa Hartling

Bibliographic record

VenueYouth & Society · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsCochraneUniversity of Alberta
FundersWomen and Children's Health Research Institute
KeywordsMental healthThematic analysisHelp-seekingInformation seekingPsychologyInformation seeking behaviorSocial mediaInformation needsQualitative researchApplied psychologySociologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Youth are vulnerable to mental health challenges. Social media presents an opportunity to evaluate disinhibited mental health discourse and self-disclosure. The objective of this study was to explore reported experiences and information needs related to youth seeking support for mental health on the social media platform, Reddit.com. We searched two subreddits: r/mental health and r/teenagers on Reddit.com for posts made by youth (13–24 years) relating to mental health help-seeking behaviors and information needs. Posts were screened and relevant data were extracted, coded, and analyzed using thematic analysis. Thematic analysis of relevant posts yielded four themes: (1) navigating mental health issues, (2) disclosing to others, (3) barriers to seeking care, and (4) experiences seeking care. Youth may have a diverse range of mental health help-seeking-related information needs and may face several barriers throughout the process of seeking care.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0030.003
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.340
Teacher spread0.301 · 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 designQualitative
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

Citations28
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueYouth & SocietySame topicDigital Mental Health InterventionsFrench-language works237,207