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Record W3122178716 · doi:10.1108/he-04-2020-0022

Evaluating the health promoting schools in Iran: across-sectional study

2021· article· en· W3122178716 on OpenAlexaff
Mehrangiz Sartipizadeh, Vahid Yazdi‐Feyzabadi, Minoo Alipouri Sakha, Aein Zarrin, Mohammad Bazyar, Telma Zahirian Moghadam, Hamed Zandian

Bibliographic record

VenueHealth Education · 2021
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCross-sectional studyChecklistPsychologyAuditMedicineMedical educationFamily medicine

Abstract

fetched live from OpenAlex

Purpose Health-promoting schools have been associated with improvements in the health status of students globally. This study is a secondary analysis study assessing Iranian HPSs. Design/methodology/approach This was a cross-sectional study on routinely collected data using an external audit 63-item checklist, which was utilized to evaluate 440 HPSs between 2014 and 2017. The mean score for each of the checklists' components was calculated. Nonparametric tests were conducted to investigate the association between the presence of a school caregiver, students' educational level and the school's score. Findings While the number of five- and four-star schools increased significantly, one- to three-star schools declined. Providing clinical and counseling services had negative growth. Despite the steady growth of the staff's health, this category still had the lowest score among; on the contrary, physical activity had the highest score in 2017. The presence of a full-time school caregiver and middle schools were both significantly correlated with achieving higher scores ( p < 0.005). Originality/value It seems that in addition to developing school facilities to promote physical activities, measures should be taken to promote access to counseling services, considering health issues of students and staff and finally increasing the number of full-time school caregiver

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)0.013
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 score0.102
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.355
GPT teacher head0.642
Teacher spread0.287 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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