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Record W2743937507 · doi:10.1093/heapro/dax039

A translational approach to characterization and measurement of health-promoting school ethos

2017· article· en· W2743937507 on OpenAlexafffund
Tarra L. Penney, Jessie‐Lee D. McIsaac, Kate Storey, Julia Kontak, Nicole Ata, Stefan Kuhle, Sara Kirk

Bibliographic record

VenueHealth Promotion International · 2017
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of AlbertaDalhousie University
FundersCanadian Institutes of Health ResearchDepartment of Health, Western Cape GovernmentNova Scotia Department of Health and WellnessHeart and Stroke Foundation of Canada
KeywordsEthosCharacterization (materials science)PsychologyMedicineEnvironmental healthPolitical sciencePhysics

Abstract

fetched live from OpenAlex

A health promoting schools (HPS) approach is hypothesized to influence student health and wellbeing by promoting a 'school ethos' that reflects the physical environment, social relations, organisational structure, policies and practices within schools. This complex set of factors makes health promoting school ethos (HPSE) challenging to define and measure. This work sought to theorise, develop and pilot a measure of HPSE as the context for implementation of HPS initiatives. We used a multi-method, iterative process to identify relevant HPSE concepts through triangulation of conceptual literature, existing tools and the tacit knowledge of school stakeholders. The HPSE measurement tool was administered to 18 elementary schools through a principal and teacher survey and an environmental assessment, followed by the development of HPSE scores for each school. Testing for internal consistency of items was used to examine theorized concepts, and scores for each school are summarised. HPSE included eight conceptual dimensions with internal consistency ranging from α = 0.60 to α = 0.87. Total HPSE scores across schools (N = 18) ranged from 1 to 8 (mean = 3.94, SD = 2.1), with 28-65% of schools reporting 'high' on respective HPSE dimensions. Schools included a heterogeneous mixture of HPSE scores, particularly across different dimensions. Our novel approach to tool development allowed us to conceptualize HPSE using a flexible process comprising different types and sources of evidence. The HPSE tool holds potential for identification and measurement of critical components of different school context as it relates to HPS.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.522
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 integrity0.0000.000
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 head0.232
GPT teacher head0.490
Teacher spread0.258 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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