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Record W4405676849 · doi:10.2196/67454

Teen Perspectives on Integrating Digital Mental Health Programs for Teens Into Public Libraries (“I Was Always at the Library”): Qualitative Interview Study

2024· article· en· W4405676849 on OpenAlexvenueno aff
Ashley A. Knapp, Katherine Cohen, Kaylee Payne Kruzan, Rachel Kornfield, Miguel Herrera, Aderonke Bamgbose Pederson, Sydney Lee, Kathryn Macapagal, Chantelle A Roulston, K. Clarke, Clarisa Wijaya, Latonia Jackson, Sandra Franco, David C. Mohr

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsPreprintQualitative researchMental healthLibrary scienceMedical educationPsychologyWorld Wide WebComputer scienceSociologyMedicinePsychiatrySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Rising rates of anxiety among teens necessitate innovative approaches for implementing evidence-based mental health support. Public libraries, seen as safe spaces for patrons with marginalized identities, offer free public services such as broadband internet access. Many teens spend significant amounts of time in their local libraries due to the safety of this space as well as the trusted adults working there. The American Library Association has shifted its priorities to focus more on mental health through employing social workers and providing mental health programs. As such, public libraries may be promising sites for the implementation of digital mental health (DMH) programs for teens. OBJECTIVE: This study aimed to examine how teens who attended their local public library experienced and managed their anxiety, what mental health supports they were interested in receiving, and how DMH programs and public libraries can meet their needs. METHODS: We interviewed 16 teens aged 12-18 (mean 15.2, SD 2.0) years who used the library frequently at the time of the interviews. Of these teen patrons, 56% (9/16) identified as female, 31% (5/16) identified as male, and 12% (2/16) identified as nonbinary. Most (11/16, 69%) identified as either White or Black or African American individuals, with the remainder (5/16, 31%) identifying as Hispanic or Latino or Chinese American individuals or with ≥2 races. The interviews were individual and semistructured, designed to elicit recommendations for designing and implementing digital tools in libraries to improve teen mental health. Interview transcripts were coded by multiple coders using thematic analysis to synthesize key themes. RESULTS: Teens reported experiencing uncontrollability, unpredictability, and anger related to their anxiety, which they managed using strategies such as guided breathing, distress tolerance, and social connection. They also talked about other helpful management techniques (eg, progressive muscle relaxation, journaling, and mood tracking). Teens underscored the importance of pairing mood tracking with daily activities to reveal patterns. They also stressed the significance of context and anxiety severity when choosing anxiety management strategies. Teens underscored the centrality of the public library in their lives and their view of it as a safe space where they can easily access resources and connect with friends and trusted adults. When considering the design of a DMH program implemented in libraries, they suggested including personalization for different identities, gamification, and simple navigation. Teens emphasized the importance of protecting their privacy within digital programs and that their end goal was to use the skills learned in the DMH program offline. CONCLUSIONS: Teens who frequently used their local public library expressed interest in receiving digital tools via libraries to help them manage anxiety. Their recommendations will help inform future research on the adaptation and implementation of DMH programs for teens in public libraries.

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)0.011
metaresearch head score (Gemma)0.012
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 score0.013
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.005
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.231
GPT teacher head0.554
Teacher spread0.323 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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