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Developmental Targeted Prevention of Conduct Disorder

2016· reference-entry· en· W2595007886 on OpenAlexaff
Frank Vitaro, Richard E. Tremblay

Bibliographic record

VenueOxford Research Encyclopedia of Criminology and Criminal Justice · 2016
Typereference-entry
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsNormativePerspective (graphical)PsychologyDevelopmental psychologyPsychological interventionDevelopmental stagePolitical sciencePsychiatryComputer science

Abstract

fetched live from OpenAlex

Abstract Traditionally, the term targeted prevention refers to interventions designed to prevent the development of adjustment problems in individuals by reducing risk factors or by implementing protective factors identified in studies of human development. Because risk and protective factors vary with development, a developmental perspective is necessary in order to identify which factors are most relevant at each period of life, based on well-defined and empirically supported etiological models. Moreover, because prevention strategies vary greatly depending on the factors that are targeted at different developmental periods and ages, a developmental perspective suggests that they need to be shaped accordingly. A further expansion of the concept of developmental targeted prevention includes the notion of “stepwise continuous prevention” for the extreme cases who do not revert to normative behavior during a given developmental period. This notion draws on the chronic-disease model of conduct problems and encompasses several developmental periods. The current debate around these issues is important as they apply to the prevention of conduct problems in youth by targeting risk factors during maternal pregnancy, early childhood, childhood, and adolescence. A consensual view of developmental targeted prevention is, however, necessary for prevention efforts to be coordinated and fruitful.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.839
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.131
GPT teacher head0.391
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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2016
Admission routes1
Has abstractyes

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