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Record W6908837112 · doi:10.32469/10355/96096

The effect of school culture on substance use : a quantitative study focusing on upper elementary students attending Adventist schools in Canada

2023· dissertation· en· W6908837112 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
Fundersnot available
KeywordsSeventh day adventistSubstance usePrivate schoolProtestantismPopulationPositive correlationNegative correlation

Abstract

fetched live from OpenAlex

Adolescents are likely to experiment with risky behaviors, such as substance use, which can have implications lasting throughout their lives. Private schools, while not immune to substance use, tend to report lower levels of substance use among their students when compared to the adolescent population in general. This quantitative study focuses on Adventist Education, a private, Protestant school system, to determine if aspects of Adventist school culture (i.e., religious experiences and school connectedness) have a salubrious effect against the engagement in substance use for upper elementary students. The data comes from the Adventist School Culture and Value Inventory conducted at Adventist schools in Canada. This research revealed the importance of developing relationships with students as those relationships have a salubrious effect on substance use among students; however, these are not statistically significant results. A statistically significant correlation does exist between teacher modeling of Christian behavior and a lower engagement in substance use. A second statistically significant correlation raises awareness for the rate of substance use among students from the First Nations population.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0070.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.399
Teacher spread0.368 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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