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Record W7164893517 · doi:10.14288/cjur.v10i2.200101

A literature review: Exploring barriers to Canadian youth mental health supports and services

2024· article· en· W7164893517 on OpenAlexaffabout
Rayyah Sempala, Margot Jackson, Vera Caine, Jinny Menon

Bibliographic record

VenueOpen Collections · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of VictoriaMacEwan University
Fundersnot available
KeywordsMental healthStigma (botany)DemographicsMental illnessMental health serviceHelp-seekingAffect (linguistics)

Abstract

fetched live from OpenAlex

Mental health for Canadian youth is an increasingly worsening issue. For many young people who are struggling with their mental health, securing meaningful and appropriate mental health support is challenging. There are significant barriers to accessing mental health services. Barriers can include, but are not limited to, supports available, wait-times, cost, social stigma, and systemic discrimination. For youth belonging to vulnerable communities, these barriers can be exacerbated by social demographic factors (e.g., gender, race). Supports which do not address the unique needs of diverse youth can contribute to poor mental health. Individuals and families often incur large expenses when attempting to access services. The persistence of stigma associated with mental illness can make young people feel increasingly isolated and alienated from peers, family and community members. More research is needed on how to improve service design as well as inputting holistic measures to break down barriers for youth seeking mental health support. In this literature review, we explore key research on the demographics of Canadian youth seeking mental health help, focusing on barriers such as long wait times, service design and community-based options that affect access and outcomes.

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)0.009
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.087
Threshold uncertainty score0.629

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0350.063
Science and technology studies0.0060.002
Scholarly communication0.0080.003
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.047
GPT teacher head0.370
Teacher spread0.322 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

Explore more

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