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Record W3128899096

School-based Physical Activity Interventions in High Schools: The Perceptions of School Stakeholders

2021· article· en· W3128899096 on OpenAlexaff
M. Dubuc, Sylvie Beaudoın, Félix Berrigan, Sylvain Turcotte

Bibliographic record

VenueRevue phénEPS / PHEnex Journal · 2021
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPsychological interventionContext (archaeology)Focus groupCategorizationPrioritizationPerceptionMedical educationPsychologySocial ecological modelResource (disambiguation)Physical activityMedicineBusinessProcess managementMarketingGeographyComputer science
DOInot available

Abstract

fetched live from OpenAlex

This study aimed to identify the facilitating factors, barriers, needs and priorities of high school stakeholders in relation to the implementation of physical activity interventions targeting students in school context. A total of 66 school stakeholders participated in individual semi-structured interviews. Thereafter, 23 of these 66 participants participated in focus groups where they needed to reach consensus on the prioritization of their community's needs. The data collected were then classified according to the five levels of factors of the socio-ecological model. Results indicate that most of the facilitating factors, barriers, needs, and priorities identified was related to the institutional level. Moreover, our results show that the needs reported are specific to each school environment. This study resulted in the categorization of facilitating factors, barriers, needs and priorities, which becomes an essential resource in the development of interventions aimed at encouraging the practice of physical activities by students in school context.   Keywords: school context; physically active lifestyle; socio-ecological model; Facilitating factors; barriers.

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)0.000
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 score0.586
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.089
GPT teacher head0.340
Teacher spread0.251 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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