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Record W7084047732 · doi:10.20381/ruor-31422

Discrimination, Mental Health, Help Seeking and Service Utilization Among Black Students in Ontario Schools

2025· dissertation· en· W7084047732 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Ottawa - Library · 2025
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Stratigraphy of Fossils
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthHelp-seekingAssociation (psychology)Mental health serviceEmotional supportPeer supportService (business)

Abstract

fetched live from OpenAlex

In the present study, I investigated the association between discrimination and the mental health of Black Ontario students in Grades 4 to 12 and explored Black students' use of school-based mental health support and its potential moderating effects. A cross-sectional study was conducted with 646 Black students (mean age = 12.51, SD = 2.34; 52.1% girls, 47.9% boys) drawn from the Health and Peer Relations Study. Data analysis included descriptive statistics, correlations, chi-square tests, and regression analyses. Gender, grade level, and mental health support were examined as moderators of the association between discrimination and mental health outcomes. Results indicated that discrimination was significantly associated with poorer mental health. Girls reported higher scores for emotional problems (70.7%) than boys (29.3%). Despite these challenges, only 16.0 % of Black students reported interest in consulting a school mental health professional in the past year. The most frequently accessed source of support was friends of the same age (girls: 22.3%, boys: 19.4%). Discussing emotional problems was linked to better mental health. These association between discrimination and Black students' mental health highlights an urgent need for culturally responsive, accessible mental health services in schools in Ontario. Policies should address both discrimination and low help-seeking behaviour, particularly among those facing chronic discrimination.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.221
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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