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Record W1973751520 · doi:10.7870/cjcmh-2001-0016

Doing Better with “Bad Kids”: Explaining the Policy-Research Gap with Conduct Disorder in Canada

2001· article· en· W1973751520 on OpenAlexaffvenueabout
Charlotte Waddell, Jonathan Lomas, Dan Offord, Mita Giacomini

Bibliographic record

VenueCanadian Journal of Community Mental Health · 2001
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsMcMaster UniversityCanadian Foundation for Healthcare ImprovementUniversity of British Columbia
Fundersnot available
KeywordsConduct disorderPsychological interventionMental healthPsychologyPublic economicsPolitical sciencePublic relationsPsychiatryEconomics

Abstract

fetched live from OpenAlex

Conduct disorder (severe and persistent antisocial behaviour in children and youth) is an important community mental health problem in Canada and has been the focus of considerable recent public policy debate. Good research evidence is available on effective (and ineffective) interventions for conduct disorder. Paradoxically, however, relatively little of the research evidence is incorporated into policy decision-making. There is a policy-research gap. An example (Hamilton, Ontario) is used to illustrate this gap. The gap is then explained using a framework for health policy analysis that incorporates values, institutional structures, and information. Values and institutional structures greatly outweigh research evidence in influencing current Canadian policy-making for the problem of conduct disorder. Possibilities for improving the situation are suggested. The youth who sprang his girlfriend from Arrell Youth (secure custody) Centre has a record many career criminals would envy. Unfortunately for society, he just turned 15 and has discovered an interest in illegal handguns. “This is a one-kid crime wave,” said a Hamilton-Wentworth police officer who spoke on condition of anonymity. “He's not like other child criminals—not even close. He's a bad kid.” That kid is in police custody today (Herron, 1995).

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.007
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 head0.193
GPT teacher head0.466
Teacher spread0.273 · 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

Citations7
Published2001
Admission routes3
Has abstractyes

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