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Record W4414685661 · doi:10.2196/64531

Improving Digital Mental Health Services With and for National Minority, Indigenous, and Refugee Youth in Norway: The InvolveMENT Multiphase Mixed Methods Research Project Protocol

2025· article· en· W4414685661 on OpenAlexvenueno aff
Petter Viksveen, Eline Ree, Stig Bjønness, Ketil Lenert Hansen, Laia G. Meldahl, Lou Krijger-Plagnol, Clare Relton, Francesca Cornaglia, Siv Hilde Berg, Solveig Hodne, Jo Røislien, Karina Aase, Anita Salamonsen

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthRefugeeProtocol (science)Health careMental health serviceMental health careMultimethodologyHealth servicesProgram evaluation

Abstract

fetched live from OpenAlex

Background: Worldwide, minority youth receive culturally sensitive mental health services less often than the majority peer population. In Norway, limited research exists on the mental health and service use among youth from national minority (Forrest Finns, Kven or Norwegian Finns, Jews, Roma, and Romani), Indigenous (Sámi), and refugee backgrounds. Although the Norwegian government provides a public communication channel for youth, including mental health information and support, digital services have not been adapted to meet the needs of these groups. There is currently no research to determine the use, acceptability, effectiveness, cost-effectiveness, and safety of these services for these youth. Objective: The main aim of the InvolveMENT project is to improve the mental health of national minority, Indigenous, and refugee youth. The project's objectives are, for these groups of youth, to (1) determine the mental health and digital support needs and possible barriers to and facilitators of service use; (2) assess the use of and satisfaction with digital services to meet their mental health needs; (3) explore their perspectives on digital mental health services; (4) develop recommendations that can be used to adapt digital services to meet their needs and rights; and (5) assess the use, acceptability, satisfaction, effectiveness, cost-effectiveness, and safety of adapted services. Methods: The 4-year InvolveMENT project consists of four phases: (1) establishing a longitudinal cohort consisting of national minority, Indigenous, and refugee youth, using surveys to assess their mental health, well-being, digital support needs, use and satisfaction with digital services, and possible barriers to and facilitators of service use; (2) conducting qualitative interviews with minority youth to explore their perspectives and synthesizing data from phases 1 and 2 for a mixed methods analysis; (3) involving youth and health care and other professionals to develop proposals to adapt and improve the existing digital services; and (4) a randomized controlled trial and a qualitative study to evaluate the adapted services. Results: Cohort and qualitative study designs have been completed. Ethics applications have been approved, and recruitment to the cohort and qualitative studies has started. Conclusions: The InvolveMENT project has the potential to enhance the accessibility and quality of health care services and early interventions, reduce inequality in service provision for minority groups, and strengthen collaboration between youth, public, and research organizations. Through this, it has the potential to improve the mental health of youth from these groups. The findings might be transferable to other minority groups, both nationally and internationally.

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)0.075
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.075
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.023
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.002
Science and technology studies0.0080.003
Scholarly communication0.0040.003
Open science0.0050.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0240.004

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 head0.310
GPT teacher head0.661
Teacher spread0.350 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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