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Record W4323648376 · doi:10.2196/43115

Mental Health Practitioners’ and Young People’s Experiences of Talking About Social Media During Mental Health Consultations: Qualitative Focus Group and Interview Study

2023· article· en· W4323648376 on OpenAlexvenueno aff
Jane Derges, Helen Bould, Rachael Gooberman‐Hill, Paul Moran, Myles-Jay Linton, Raphael Rifkin‐Zybutz, Lucy Biddle

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
FundersArts and Humanities Research CouncilEconomic and Social Research CouncilMedical Research CouncilNational Institute for Health and Care Research
KeywordsMental healthFocus groupThematic analysisSocial mediaQualitative researchPsychologyMedicineNursingPsychiatrySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Increasing concerns among mental health care professionals have focused on the impact of young people's use of digital technology and social media on their mental well-being. It has been recommended that the use of digital technology and social media be routinely explored during mental health clinical consultations with young people. Whether these conversations occur and how they are experienced by both clinicians and young people are currently unknown. OBJECTIVE: This study aimed to explore mental health practitioners' and young people's experiences of talking about young people's web-based activities related to their mental health during clinical consultations. Web-based activities include use of social media, websites, and messaging. Our aim was to identify barriers to effective communication and examples of good practice. In particular, we wanted to obtain the views of young people, who are underrepresented in studies, on their social media and digital technology use related to mental health. METHODS: A qualitative study was conducted using focus groups (11 participants across 3 groups) with young people aged 16 to 24 years and interviews (n=8) and focus groups (7 participants across 2 groups) with mental health practitioners in the United Kingdom. Young people had experience of mental health problems and support provided by statutory mental health services or third-sector organizations. Practitioners worked in children and young people's mental health services, statutory services, or third-sector organizations such as a university counseling service. Thematic analysis was used to analyze the data. RESULTS: Practitioners and young people agreed that talking about young people's web-based activities and their impact on mental health is important. Mental health practitioners varied in their confidence in doing this and were keen to have more guidance. Young people said that practitioners seldom asked about their web-based activities, but when asked, they often felt judged or misunderstood. This stopped them from disclosing difficult web-based experiences and precluded useful conversations about web-based safety and how to access appropriate web-based support. Young people supported the idea of guidance or training for practitioners and were enthusiastic about sharing their experiences and being involved in the training or guidance provided to practitioners. CONCLUSIONS: Practitioners would benefit from structured guidance and professional development to enable them to support young people in feeling more willing to disclose and talk about their web-based experiences and their impact on their mental health. This is reflected in practitioners' desire for guidance to improve their confidence and skills to safely support young people in navigating the challenges of the web-based world. Young people want to feel comfortable discussing their web-based activities during their consultations with mental health practitioners, both in tackling the challenges and using the opportunity to discuss their experiences, gain support, and develop coping strategies related to web-based safety.

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.217
GPT teacher head0.566
Teacher spread0.349 · 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.

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

Citations20
Published2023
Admission routes1
Has abstractyes

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