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Record W7006701551

What is health promotion in Swedish upper secondary school? : An analysis of how the teacher comes across in student health plans

2021· article· en· W7006701551 on OpenAlexaboutno aff

Bibliographic record

VenueDiVA (Linnaeus University) · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse Scientific and Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHealth promotionThematic analysisCharterHealth educationHealth policyWork (physics)Public health
DOInot available

Abstract

fetched live from OpenAlex

Ever since the Ottawa charter in 1986, health promotion through schools has been stressed across the globe. In Sweden, where the present study is set, school health promotion is stipulated on a national level by the Education Act and the Health And Medical Services Act. It is emphasized in national directives that health promotion should be part of the education. The present study aims to widen our understandings of health promotion by exploring the so-called Student Health Plan (SHP) in upper secondary schools, which is a document conducted to concretize the national directives. The aim of the study is to examine how the teacher comes across in the SHPs regarding health promotion. The data consists of 37 SHPs from high schools. The SHPs were analyzed using thematic analysis and the data was coded and divided into themes. The analysis resulted in two themes: The “invisible” teacher and The visible teacher. Teachers are largely “invisible” in the SHPs and lack tasks and responsibility. When the teacher is visible, student attendance, teaching and relational work are areas of responsibility in health promotion. Standard teacher tasks are considered health promotion, which motivates the question of the necessity of a SHP. The findings also suggest that regular teaching is health promoting, strengthening the key role of the teacher in the health promoting work.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.052
GPT teacher head0.268
Teacher spread0.216 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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