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Record W2890714551 · doi:10.23889/ijpds.v3i4.931

Cross-sector service use among youth and young adults involved in the Alberta provincial justice system

2018· article· en· W2890714551 on OpenAlexaffabout
Xinjie Cui, Christine Werk

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsGovernment of Alberta
Fundersnot available
KeywordsEconomic JusticeCriminal justiceService (business)Mental healthBusinessPsychologyEconomic growthPolitical scienceCriminologyPsychiatryEconomicsMarketing

Abstract

fetched live from OpenAlex

IntroductionYouth and young adults who are involved in justice and correctional system often have complex service needs and experience poor health and social outcomes. To improve outcomes for these young Albertans, it is important to understand their characteristics and service utilization patterns across health and social areas.
 Objectives and ApproachThe current analysis provides cross-sector service use information on Alberta youth and young adults aged 12 to 25 who were involved in the criminal justice system from 2005 to 2011. Administrative data linked data across multiple Alberta provincial ministries were used for the analysis. These included over 20 programs and services in areas such as health, education, child intervention, disability supports, income support, and justice and corrections.
 ResultsOffence types and court outcomes varied by social-demographic characteristics such as age, sex, social economic status, and residential mobility. Elevated mental health and high cost health services use was observed among youth involved in the criminal justice system. Access of social programs such as Child Invention service, Child Support services, Income Support and disability support services was more prevalent among youth and young adults who were involved in the justice system. A higher number of offences was associated with worse education and health outcomes.
 Conclusion/ImplicationsCross-sector data linkage and analysis offer a unique opportunity for better understanding of clients/patients and services/programs at the broad system level. Knowledge gained through linked data can inform cross-ministry policies and integration of services.

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)0.008
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 score0.070
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.006
Open science0.0020.001
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 head0.537
GPT teacher head0.614
Teacher spread0.077 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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