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Record W2890423698 · doi:10.23889/ijpds.v3i4.660

Using administrative data to examine government service transitions of children, youth and young adults in Alberta, Canada

2018· article· en· W2890423698 on OpenAlexaboutno aff
Navjot Lamba, Ruiting Jia, Hitesh Bhatt

Bibliographic record

VenueInternational Journal for Population Data Science · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsIncome SupportGovernment (linguistics)HarmService (business)Service delivery frameworkMental healthBusinessPsychologyMedicinePolitical sciencePsychiatrySocial psychologyMarketing

Abstract

fetched live from OpenAlex

IntroductionA main goal of policy makers is to understand various transition pathways between services to optimize program design and to achieve successful outcomes, for example, securing stable income or having access to disability supports.
 Objectives and ApproachUsing linked administrative data (between 2006 and 2011), this study examines two types of program transition pathways: 1) Toward understanding who may be at risk for relying on income support, we profile Albertans transitioning from an income support training program to a program providing income support but no training, and 2) Toward understanding service delivery from child/youth to adult disability programs, we profile students receiving child/youth disability services, but who did not transition into an adult disabilities program. The socio-demographic characteristics and government service use of these students, including health and education-related services, were examined.
 ResultsFirst, among Albertans who transitioned from an income support training program to one in which only income support was provided: 57% were female; 72% were living in low socio-economic neighbourhoods; 14% were high cost health service users; 20% received mental health services; 30% received an injury/harm diagnosis; and 12% were involved in corrections. Second, among children/youth who received child disability services but did not transition to an adult disability program, lower than expected proportions received Assured Income for the Severely Handicapped (45%) and Income Support (14%), while higher than expected proportions had criminal offences (17%).
 Conclusion/ImplicationsProfiles produced from linked data helps Alberta government to understand clients’ program transitions which allows for: 1) the identification of Albertans requiring supports for securing employment that reduce or eliminate the need for income support, and 2) improvements in service delivery to individuals transitioning from child/youth to adult disability 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.209
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.216
GPT teacher head0.378
Teacher spread0.162 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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 routes1
Has abstractyes

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