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Record W1968600048 · doi:10.1177/0829573506295456

The Development and Use of the Healthy School Indicator Tool (HSIT)

2005· article· en· W1968600048 on OpenAlexaff
Jac J. W. Andrews, Richard Conte

Bibliographic record

VenueCanadian Journal of School Psychology · 2005
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCronbach's alphaPsychologyReliability (semiconductor)Norm (philosophy)Survey instrumentApplied psychologyMedical educationPsychometricsClinical psychologyMedicine

Abstract

fetched live from OpenAlex

This article describes the development and use of a norm-referenced instrument called the Healthy School Indicator Tool (HSIT) that was designed to assist educational professionals monitor their progress in addressing critical health issues in schools. Factor analyses of two administrations of the survey indicated a stable factor structure. In terms of reliability, both split-half and Cronbach’s alpha analyses revealed adequate reliability of factors. In terms of validity, the survey appears to adequately cover critical school health issues and is able to discriminate schools that have been involved in comprehensive school health programs for varying amounts of time.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.447
Teacher spread0.338 · 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 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

Citations9
Published2005
Admission routes1
Has abstractyes

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