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Record W2150835628 · doi:10.1177/1049731507313978

Evaluation of an In-Service Training Program for Child Welfare Practitioners

2008· article· en· W2150835628 on OpenAlexaff
Daniel Turcotte, Geneviève Lamonde, André Beaudoin

Bibliographic record

VenueResearch on Social Work Practice · 2008
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsCentre Jeunesse de QuebecUniversité Laval
Fundersnot available
KeywordsWelfareCompetence (human resources)Social workFeelingPsychologyMedical educationSample (material)NursingTest (biology)Training (meteorology)Applied psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

Objective: To test the effectiveness of an in-training program for practitioners in public child welfare organizations. Method: The sample consists of practitioners (N = 945) working in youth centers or in local community service centers. Data are collected through self-administered questionnaires prior to and after the program. Results: The data show that prior to the training program, there are few differences between workers according to their educational backgrounds. Following the training program, practitioners felt more competent, had acquired additional knowledge, and had changed some of their behaviors with families. If level of stress at work had slightly decreased, job satisfaction remained unchanged. Conclusion: Findings suggest that an in-service training program may contribute to increased knowledge and feeling of competence and to modified professional behaviors, but it is essential to develop more valid indicators of knowledge and skills necessary to perform child welfare 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 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)0.007
metaresearch head score (Gemma)0.015
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.489
GPT teacher head0.570
Teacher spread0.081 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

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