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Record W1982192197 · doi:10.3390/challe2010001

Using a Mobile Laboratory to Study Mental Health, Addictions and Violence: A Research Plan

2011· article· en· W1982192197 on OpenAlexafffundabout
Samantha Wells, Andrea Flynn, Kathryn Graham, Jürgen Rehm, John Cairney, Nick Kates, James L. Kennedy, Daniela S. S. Lobo, Michael Chaiton, Peter Menzies, Rachel F. Tyndale, Zulfikarali Verjee

Bibliographic record

VenueChallenges · 2011
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsHospital for Sick ChildrenLaurentian UniversityCanada Research ChairsMcMaster UniversityUniversity of TorontoCentre for Addiction and Mental HealthWestern University
FundersCanadian Institutes of Health ResearchQueen's University
KeywordsMental healthPlan (archaeology)AddictionPovertyPublic relationsPsychologyPolitical scienceGeographyPsychiatry

Abstract

fetched live from OpenAlex

This paper describes an innovative new research program, Researching Health in Ontario Communities (RHOC), designed to improve understanding, treatment and prevention of co-occurring mental health, addictions, and violence problems. RHOC brings together a multi-disciplinary team of investigators to implement an integrated series of research studies (including pilot studies and full studies). The project involves use a mobile research laboratory to collect a wide range of biological, behavioral and social data in diverse communities across Ontario, Canada, including remote and rural communities, areas experiencing poverty and social disorganization, urban areas, and Aboriginal communities. This paper describes the project background and research plan as well as the anticipated contributions of the project to participating Ontario communities and to broader scientific knowledge.

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)0.022
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.192
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0080.004
Scholarly communication0.0050.005
Open science0.0050.007
Research integrity0.0030.002
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 head0.640
GPT teacher head0.572
Teacher spread0.068 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations13
Published2011
Admission routes3
Has abstractyes

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