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Record W2089803819 · doi:10.1080/15433714.2011.581545

Implementing Evidence-Based Practice: Practitioner Assessment of an Agency-Based Training Program

2013· article· en· W2089803819 on OpenAlexaff
Sarah E. Bledsoe-Mansori, Jennifer I. Manuel, Jennifer L. Bellamy, Lin Fang, Erna Dinata, Edward J. Mullen

Bibliographic record

VenueJournal of Evidence-Based Social Work · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Mental Health
KeywordsTrainerAgency (philosophy)General partnershipMedical educationExploratory researchTraining (meteorology)PsychologyFocus groupBest practiceWork (physics)Evidence-based practiceApplied psychologyMedicineComputer scienceEngineeringManagementPolitical scienceSociologyAlternative medicine

Abstract

fetched live from OpenAlex

Responding to the call for evidence-based practice (EBP) in social work, the authors conducted a multiphase exploratory study to test the acceptability of a training-based collaborative agency-university partnership strategy supporting EBP. The Bringing Evidence for Social Work Training (BEST) study includes an agency training component consisting of 10 modules designed to support the implementation of EBP in social agencies. Qualitative data from post-training participant focus groups were analyzed in order to describe practitioner perceptions of the 10 training modules and trainer experiences of implementation. Based on the findings from this study the authors suggest that the BEST training was generally acceptable to agency team members, but not sufficient to sustain the use of EBP in practice.

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)0.016
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.829
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.004
Science and technology studies0.0040.001
Scholarly communication0.0010.007
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.210
GPT teacher head0.499
Teacher spread0.289 · 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 designOther design
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

Citations32
Published2013
Admission routes1
Has abstractyes

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