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Record W2762546449

Dear Teachers: : What Youth Living With Mental Illness Want Teachers to Know

2017· article· en· W2762546449 on OpenAlexaffabout
Melanie-Anne Atkins

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Educational Sciences
Canadian institutionsWestern University
Fundersnot available
KeywordsMental illnessMental healthMental health literacyCurriculumPsychologyLiteracyPedagogyTask (project management)Medical educationMedicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

In a climate with increased awareness about the role of mental health in the classroom, this paper explores the messages that youth living with mental illness want to send to future teachers. The findings in this paper are part of a larger study where the author partnered with youth and teacher candidates to develop curriculum for a professional development day at a Canadian Faculty of Education where teacher candidates had the opportunity to learn how they could contribute to a mentally healthy classroom in general, and support the needs of students living with mental illness in particular. Youth in this study recognized that teacher candidates can be the next generation of mental health champions in their schools by approaching their practice through an affirming, anti-discriminatory lens. At the same time, youth expressed frustration at finding their voices missing and their perspectives devalued in documents, policies, and practices intended to serve them because of disempowering stereotypes associated with people living with mental illness. Youth wanted the opportunity to contribute their lived experience to the task of shaping mental health literacy education.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience 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 score0.217
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.068
GPT teacher head0.341
Teacher spread0.272 · 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
Published2017
Admission routes2
Has abstractyes

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