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Record W4312402748 · doi:10.2196/38296

Using Web-Based Content to Connect Young People With Real-life Mental Health Support: Qualitative Interview Study

2022· article· en· W4312402748 on OpenAlexvenueno aff
Emily Adeane, Kerry Gibson

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthThematic analysisEmpowermentPsychologyContent analysisReflexivityQualitative researchMedical educationInternet privacyMedicineSociologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Young people experience high rates of mental health problems but make insufficient use of the formal services available to them. As young people are heavy users of the internet, there may be an untapped potential to use web-based content to encourage this hard-to-reach population to make better use of face-to-face mental health services. However, owing to the vast range of content available and the complexities in how young people engage with it, it is difficult to know what web-based content is most likely to resonate with this age group and facilitate their engagement with professional support. OBJECTIVE: This study aimed to identify the types of web-based content young people identified as more likely to prompt youth engagement with mental health services. METHODS: This study used a qualitative design conducted within a social constructionist epistemology that recognized the importance of youth empowerment in mental health. Digital interviews using WhatsApp instant messenger were conducted with 37 young people aged 16-23 years who participated as "expert informants" on the priorities and practices of youth in web-based spaces. The data were analyzed using reflexive thematic analysis to identify the types of web-based content that participants believed would encourage young people to reach out to a face-to-face mental health service for support. RESULTS: The analysis generated 3 main themes related to the research question. First, participants noted that a lack of information about available services and how they worked prevented young people from engaging with face-to-face mental health services. They proposed web-based content that provided clear information about relevant mental health services and how to access them. They also suggested the use of both text and video to provide young people with greater insight into how face-to-face counseling might work. Second, participants recommended content dedicated to combating misconceptions about mental health and negative portrayals of mental health services and professionals that are prevalent in their web-based spaces. They suggested content that challenged the stigma surrounding mental health and help seeking and highlighted the value of mental health services. Finally, participants suggested that young people would be more likely to respond to "relatable" digital stories of using mental health services, recounted in the context of a personal connection with someone they trusted. CONCLUSIONS: This study offers recommendations for professionals and service providers on how to better engage young people with real-life mental health support using web-based content. Web-based content can be used to challenge some of the barriers that continue to prevent young people from accessing face-to-face mental health services and underlines the importance of including young people's voices in the design of web-based mental health content.

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)0.019
metaresearch head score (Gemma)0.017
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 score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0090.009
Scholarly communication0.0040.004
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.383
GPT teacher head0.585
Teacher spread0.202 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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