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Record W4385785238 · doi:10.25236/ijnde.2023.051610

A Study of Positive Psychological Orientation in Mental Health Education for Canadian High School Students

2023· article· en· W4385785238 on OpenAlexaffabout
Tingxi Zheng

Bibliographic record

VenueInternational Journal of New Developments in Education · 2023
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsEarl Haig Secondary School
Fundersnot available
KeywordsMental healthPsychologySet (abstract data type)Medical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

To optimize the high school psychological health education guided by positive psychology, we need to highlight the main position of students, pay attention to the positive factors of students, let students have the motivation to study and live, realize the healthy development of high school students' psychological quality, and lay a solid foundation for future study and work. In recent years, the work of mental health education for high school students in Canada has been developing, and various schools have set up mental health education courses, and set up a team of full-time psychological teachers to make full use of psychological lectures, group psychological counseling and other methods to carry out mental health education. However, there are still some problems, which need to be further optimized in combination with positive psychological guidance. Canada's high school education has been developing at a high speed. In the process of teaching, it is necessary to improve students' interest in learning, pay attention to guiding students' mental health, and promote students' all-round development. This paper discusses the optimization of high school mental health education guided by positive psychology, clarify the development ideas of high school mental health education, improve students' interest in learning, promote students' physical and mental health development, and realize students' all-round development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.524
Teacher spread0.478 · 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
Published2023
Admission routes2
Has abstractyes

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