Use It or Lose It? Predicting Learning Transfer of Relationship and Marriage Education Among Child Welfare Professionals
Bibliographic record
Abstract
Objective Following a training in relationship and marriage education (RME), examine whether applying information at 2 months is associated with application at 6 months and how participants' confidence, utility, and self‐efficacy is associated with learning transfer and application at 2 months posttraining. Background Child welfare professionals are required to receive numerous trainings each year with the expectation of understanding, retaining, and transferring this learning into practice. Method With a sample of 324 child welfare professionals across 5 states who completed a 1‐day training in RME, we used structural equation modeling with participant self‐efficacy, utility, and confidence as predictors of application of RME concepts at 2 months posttraining. We also assessed how application of RME concepts at 2 months predicted self‐efficacy, confidence, and application at 6 months. Results Only the combined effect of both higher self‐efficacy and higher utility was related to applying concepts at 2 months. Those who apply the concepts at 2 months are more likely both to report higher confidence at 6 months and to apply the concepts at 6 months. Conclusions Evaluations of trainings should move beyond measurement of immediate learning outcomes to better understanding how to motivate immediate learning transfer. Implications If participants do not feel like they have actually learned new skills and, more importantly, do not implement the skills with individuals or clients soon after a training, they will be much less likely to use them in the future. A combination of learning concrete principles and skills with confidence they can implement the materials may result in future implementation.
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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".