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Record W3110823008

Reclaiming Student Voices on School-based, Mental Health Impacts: Youth Reflections, Lived Experiences and Recommendations for Transforming Ontario Secondary Schools

2020· dissertation· en· W3110823008 on OpenAlexaboutno aff
Dustin Nathaniel Graham

Bibliographic record

VenueYork University Digital Library (York University) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthStigma (botany)SilencePsychologyPositive Youth DevelopmentPublic healthPedagogyDevelopmental psychologyMedicineNursingPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

In recent years, student mental health has gained considerable attention in Ontarios secondary schools. While the voices of students are central to the discussion about school-based mental health impacts, they are often undervalued or missing altogether in academic and policy literature on the topic. This phenomenological study reclaims and thematically analyzes the reflections of 11 youth participants on the cultural features of their Ontario public secondary school that impacted their mental health. The critical and conceptual theories of Michel Foucault (1988), Paulo Freire (2000), and Corey Keyes (2002) inform this dissertations theoretical framework. Three main findings were revealed in this study: (1) a gendered and stigma-laden culture of silence had a significant mental health impact on the youth in their secondary school; (2) the youth identified a number of supportive and unsupportive educational practices as impactful; and, (3) peer relationships were also shown to be supportive and unsupportive features with mental health impact for the youth, with an emphasis on bullying and peer-support groups. The youth recommendations presented in this study have profound implications for how to best support the mental health of secondary-school-aged youth in Ontario moving forward.

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 categoriesMeta-epidemiology (narrow), Science 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 score0.419
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.281
Teacher spread0.242 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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