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Record W1829415611 · doi:10.5539/ies.v8n9p148

The Development of a Secondary School Health Assessment Model

2015· article· en· W1829415611 on OpenAlexvenueno aff
Srinual Sriring, Prawit Erawan, Monoon Sriwarom

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsnot available
Fundersnot available
KeywordsSchool healthSecondary educationPsychologyHealth assessmentMedical educationPublic healthMathematics educationMedicineNursing

Abstract

fetched live from OpenAlex

The objective of this research was to: 1) involved a survey of information relating to secondary school health, 2) involved the construction of a model of health assessment and a handbook for using the model in secondary school, 3) develop an assessment model for secondary school. The research included 3 phases. (1) involved a survey of information relating to secondary school health, which was performed by analyzing the approach and reviewing the related literature. The phase also involved synthesizing the factors associated with health in secondary school. (2) involved the construction of a model of health assessment and a handbook for using the model in secondary school. (3) the health assessment model for secondary school was applied to large, medium, and small schools to evaluate the model’s validity. In addition, the assessment model was evaluated based on its utility, feasibility, propriety, and accuracy. The research findings suggested the following: 1) The health assessment model for secondary school consisted of 4 major factors, 13 sub-factors, and 68 indicators. 2) The health assessment model for secondary school was deemed by experts to have content validity. 3) The health assessment model for secondary school was considered to have high levels of utility, feasibility, propriety, and accuracy. Considering each aspect, it was found that the aspects of utility, feasibility, propriety, and accuracy, were “High” for every aspect.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.338
GPT teacher head0.643
Teacher spread0.305 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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