TOWARDS A MORE COMPREHENSIVE UNDERSTANDING OF FOSTERING CONNECTIONS: THE TRAUMA-INFORMED FOSTER CARE PROGRAMME: A MIXED METHODS APPROACH WITH DATA INTEGRATION
Bibliographic record
Abstract
Foster carers require high-quality training to support them in caring for children with trauma-related difficulties. This paper describes a mixed methods approach that was applied to evaluate the complex intervention Fostering Connections: The Trauma-Informed Foster Care Programme, a recently developed trauma-informed psychoeducational intervention for foster carers in Ireland. A quantitative outcome evaluation and a qualitative process evaluation were integrated to capture a comprehensive understanding of the effects of this complex intervention. A convergent mixed methods model with data integration was used. Coding matrix methods were employed to integrate data. There was convergence among component studies for: programme acceptability, increased trauma-informed foster caring, improvement in child regulation and peer problems, and the need for ongoing support for foster carers. This research provides support for the intervention suggesting the importance of its implementation in Ireland. The integrative findings are discussed in relation to effects and future implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".