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Record W2098950772 · doi:10.1177/1049731511406552

Promoting Evidence-Informed Practice in Child Welfare in Ontario

2011· article· en· W2098950772 on OpenAlexaffabout
Wes Shera, Katharine Dill

Bibliographic record

VenueResearch on Social Work Practice · 2011
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFormative assessmentFeelingPublic relationsWelfareEvidence-based practicePsychologyFunction (biology)Medical educationPolitical scienceMedicinePedagogySocial psychologyAlternative medicine

Abstract

fetched live from OpenAlex

Practice and Research Together (PART) is an Ontario-based research utilization initiative, the core function of which is to distil and disseminate practice-relevant research findings to child welfare practitioners. This article addresses (a) the mission and goals of the PART program; (b) the key components of the program design; (c) the conceptual foundations of evidence-informed practice (EIP) as it relates to the program; (d) the successes and challenges of implementation to date; (e) the results of a comprehensive evaluation; and (f) areas for future research and development. Key findings of the formative evaluation include Link PARTners (LPs—organizational representatives) feeling isolated in their role in promoting organizational change; front-line practitioners reporting that they have little time or resources to use the program materials; supervisors stating that they support the concept of EIP but lack the skills and abilities to move these ideas forward; and executive directors are requesting more evidence to promote organizational and systemic change.

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)0.078
metaresearch head score (Gemma)0.113
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.935

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0780.113
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0130.011
Scholarly communication0.0090.003
Open science0.0040.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.655
GPT teacher head0.631
Teacher spread0.024 · 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 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

Citations20
Published2011
Admission routes2
Has abstractyes

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