MétaCan
Menu
Back to cohort
Record W4237990829 · doi:10.24124/2014/bpgub1675

A Person First: a workshop to help teens support friends with mental problems

2014· dissertation· en· W4237990829 on OpenAlexaboutno aff
Debra G. Edzerza

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthStigma (botany)Transformative learningPsychologyContext (archaeology)Culturally appropriateMedical educationPsychological resiliencePeer supportPedagogyPublic relationsPolitical scienceMedicineSocial psychologyGerontologyPsychiatryGeography

Abstract

fetched live from OpenAlex

The author of this study, a First Nations teacher, has designed a culturally sensitive workshop for northern youth addressing mental health concerns. The ten hour workshop, entitled A Person First!, will encourage youth to consider the harmful impact of stigma on people who have mental health issues. The need for a workshop that appeals specifically to First Nations youth is evident in Yukon and in other northern communities, currently there is a lack of culturally relevant workshops that addresses mental health issues in remote northern communities. A Person First! Is geared towards First Nations learners and will be presented n the context of the cultural beliefs systems within their own communities. The author has presented a leader's guide for local facilitators that includes instructions for the use of video clips, circle discussions, and a self-reflection tool based on the Medicine Wheel. The workshop design, supported by research, recommends community education to promote youth resilience through stigmas reduction and peer support. As community-based education, this workshop was designed to stimulate transformative change in youth thinking and behavior so that peers experiencing mental health issues will experience a supportive environment.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.586
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.315
Teacher spread0.292 · 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
GenreOther

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
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicAdult and Continuing Education TopicsFrench-language works237,207