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Record W2742188258 · doi:10.5014/ajot.2017.71s1-rp303b

The Development of the Substance Use Wellness Tool on University Campuses

2017· article· en· W2742188258 on OpenAlexaff
Shu‐Ping Chen, Terry Krupa, Heather Stuart

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2017
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSubstance useMental healthPsychologyIntervention (counseling)Medical educationSubstance abuseApplied psychologyGerontologyPsychotherapistClinical psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Date Presented 4/1/2017 The Substance Use Wellness Tool raises awareness of the links among occupation, substance use, and mental health to support the well-being of young people in postsecondary settings. This study addresses how to apply the tool to facilitate early intervention. Primary Author and Speaker: Shu-Ping Chen Contributing Authors: Terry Krupa, Heather Stuart

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)0.012
metaresearch head score (Gemma)0.034
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.034
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.083
GPT teacher head0.344
Teacher spread0.260 · 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
Published2017
Admission routes1
Has abstractyes

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