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Record W4416695606 · doi:10.1186/s12889-025-25136-3

Young people’s experiences and strategies for improving their awareness and access to mental health and substance use health services

2025· article· en· W4416695606 on OpenAlexafffundabout
Ashley D Radomski, Christine Polihronis, Paula Cloutier, Charlene Shujie Song, Kayla Beaudin, Matthew Menear, Natasha Saunders, Rachelle Ashcroft, Mario Cappelli

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsHospital for Sick ChildrenCanadian Medical AssociationCanadian Association of Occupational TherapistsOntario Centre of Excellence for Child and Youth Mental HealthUniversité LavalUniversity of TorontoAgricultural Research Institute of Ontario
FundersCanadian Institutes of Health Research
KeywordsMental healthGeneral partnershipReferralBiostatisticsPublic healthAutonomyService (business)Substance useService delivery framework

Abstract

fetched live from OpenAlex

BACKGROUND: Young people face persistent challenges in accessing mental health and substance use health (MHSU) services, which have been further complicated by the COVID-19 pandemic, and are particularly pronounced among marginalized populations. We sought to understand how young people navigate MHSU services since the pandemic, focusing on their awareness of and access to care. METHODS: Our project uses participatory research approaches and is comprised of two phases. This manuscript describes Phase 1, where five young people participated as research partners, informing study design and implementation. We conducted five virtual focus groups with a larger sample of young people from Ontario with MHSU lived experience, prioritizing participants from underrepresented communities. Focus groups explored their service awareness, access experiences, and improvement suggestions. Discussions were audio-recorded, transcribed, and thematically analyzed. Phase 2 will involve co-designing resources for young people based on Phase 1 findings. RESULTS: Our analysis of forty participants identified six interconnected domain themes (relationships and guidance, preferences and choice, convenience, self-directed information seeking, established sources, and system constraints), and fifteen sub-themes, spanning three dimensions of MHSU services (awareness, access, and improvements). We found that young people relied on informal networks (family/friends) and healthcare providers for MHSU service information and access, with trust being essential. They desired both choice in their provider and delivery method, plus the ability to research options independently before making decisions with others. While they primarily got service information from healthcare providers, online platforms, and community organizations, finding comprehensive, reliable information remained challenging. Barriers such as costs, wait times, location, and discrimination highlighted the need for affordable, accessible, and culturally responsive care to meet their diverse needs. CONCLUSIONS: This study revealed key insights about post-pandemic MHSU service awareness and access for young people. Participants demonstrated desires for both autonomy in service selection and guidance from trusted sources, suggesting a supported decision-making model. Recommended service improvements include centralized information resources, stronger informal referral networks, culturally responsive services and flexible delivery options. Partnership with young people throughout the research process yielded valuable perspectives that enhanced study validity. These findings will inform Phase 2-the co-design of resources addressing the information gaps and structural barriers identified by participants.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
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 head0.241
GPT teacher head0.461
Teacher spread0.220 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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