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Record W2782783389 · doi:10.18357/ijcyfs83/4201718001

THE USE OF ADMINISTRATIVE DATA TO STUDY THE TRIPLE P – POSITIVE PARENTING PROGRAM

2017· article· en· W2782783389 on OpenAlexafffundvenueabout
Rübab G. Arım, Anne Guèvremont, V. Susan Dahinten, Dafna Kohen

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2017
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsUniversity of British ColumbiaStatistics Canada
FundersOttawa Hospital Research Institute
KeywordsMental healthPopulationIntervention (counseling)OddsPsychologyCommunity healthGerontologyMedicineEnvironmental healthPublic healthNursingPsychiatryLogistic regression

Abstract

fetched live from OpenAlex

This study is among the first Canadian population-based evaluations designed to examine associations of the Triple P – Positive Parenting Program (Triple P) for mother and child outcomes at the community level. Uniquely, this study was conducted independently of program implementation, using data collected for other purposes. Three anonymized British Columbia provincial administrative data sources were used, in addition to program data collected by administrators (Island Health). The study employed a quasi-experimental design to examine benefits of Triple P at the community level, and used sociodemographic community characteristics to match 11 target communities where Triple P was implemented with comparison communities where Triple P was not implemented. The study’s design and analyses took into account the phased-in implementation of Triple P across Vancouver Island (2004–2008), drawing on pre- and post-implementation data for all of the studied communities. Hierarchical linear modeling results showed that children living in communities where the program had been administered were more likely to have been diagnosed with conduct disorders and to have used counseling services. Program intensity was not associated with any of the child health outcomes. For mothers, higher program intensity was associated with lower odds of being diagnosed with mental health conditions. Future research should continue to demonstrate the feasibility of a population-based approach and the use of secondary data along with program data to examine community-based intervention programs.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.597
GPT teacher head0.563
Teacher spread0.034 · 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

Citations1
Published2017
Admission routes4
Has abstractyes

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