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Record W1564797652 · doi:10.1300/j146v04n01_08

Canadian Child Welfare Outcomes Indicator Matrix

2000· article· en· W1564797652 on OpenAlexaboutno aff
Nico Trocmé, Bruce MacLaurin, Barbara Fallon

Bibliographic record

VenueJournal of Aggression Maltreatment & Trauma · 2000
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsWelfareMandateLegislationTracking (education)Service delivery frameworkPublic economicsService (business)PsychologyPublic relationsBusinessActuarial sciencePolitical scienceEconomicsMarketingLaw

Abstract

fetched live from OpenAlex

The Client Outcomes in Child Welfare (COCW) Project was designed to examine the state of knowledge about outcomes measurement in Canada and initiate a consensus building process for a coordinated strategy in tracking outcomes across Canada. Through interviews with key informants, reviews of the literature and analysis of legislation, policy documents and information systems, the COCW Project identified some of the challenges to outcome measurement that may explain the limited progress made in this field. These include a needs driven service delivery system, competing objectives of child welfare, definitional confusion, and differences between clinical and administrative use of outcomes. The Outcomes Indicator Matrix was developed as a first stage in a strategy that focuses initially on the administrative use of outcomes information, allowing the clinical use of outcomes measures to develop in a more gradual fashion. Indicators were selected in four domains which reflect the breadth of the child welfare mandate in Canadian jurisdictions: child protection, child functioning, permanence and continuity of care for the child, and family and community support.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.063
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0210.026
Science and technology studies0.0040.001
Scholarly communication0.0040.001
Open science0.0030.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.004

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 head0.012
GPT teacher head0.297
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations11
Published2000
Admission routes1
Has abstractyes

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