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Record W7133028627

Looking through the Teacher's Lens: Ontario Secondary Teachers' Experiences of Addressing the Needs of Students with a Mental Health Problem

2017· other· en· W7133028627 on OpenAlexaffabout
May Lu

Bibliographic record

VenueTSpace · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthQualitative researchSecondary educationHelp-seekingProfessional developmentSchool teachers
DOInot available

Abstract

fetched live from OpenAlex

This qualitative study examined how secondary school teachers view their role in addressing the mental health needs of their students. Interviews were conducted with three secondary school teachers in the Ontario publically funded system to understand their experiences in a classroom supporting students with mental health problems. The findings revealed that participants relied on a school-based team to support any student with a mental health problem. Classroom teachers felt that they were responsible for constant observation and could best support their students by building genuine and authentic relationships. Difficulties and challenges to addressing students were noted such as policy limitations and lack of training. All participants were optimistic about the future of addressing mental health in Ontario secondary schools as they have noticed a change in the way schools and school boards are providing opportunity for teacher education and professional development to be better support students with a mental health problem.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.231
Threshold uncertainty score0.466

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.014
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.105
GPT teacher head0.413
Teacher spread0.309 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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