Impact of work environment on training transfer, child welfare workers' experiences
Bibliographic record
Abstract
Inservice training has gained popularity in the field of child welfare.program planning literature suggests that evaluation should be included in any program plan.Unfortunately, evaluation is often a missing component in in-service training programs.When conducted, evaluations frequently focus on the training event itself and stop short of assessing whether training participants have applied the training on the job.The work environment is increasingly recognized as impacting successful transfer of training.Grounded theory methodology was used in this largely qualitative evaluation of transfer of Competency-Based Inservice Training (CBIT) at Winnipeg Child and Family Services (WCFS).Post training evaluations were analysed to determine work environment factors that may inhibit transfer.One hundred and twenty social workers who had completed the CBIT at WCFS were sent the "Human Services Training Effectiveness postcard', (Curry & Chandler, 1999, p.43).Fifty-four percent (65) of the sample completed and returned the survey' Theoretical sampling was used to select twelve respondents to participate in focus group interviews.The preliminary evaluation results were shared with each focus group participant and their feedback was integrated into the final report.participants evaluate the CBIT event positively overall.They are applying parts of the training in their work, but application is inhibited by factors in their work environment.High workload is the most signifîcant barrier to their application of the training.The findings of this evaluation are discussed in light of the literature regarding the competency-based approach, social work education and training in child welfare, and management of inservice training programs.Finally, some recommendations to improve the transfer of CBIT at WCFS are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".