Good helping relationships in child welfare: learning from stories of success
Bibliographic record
Abstract
ABSTRACT This study involved in‐depth exploration of good helping relationships in child welfare. A select sample of six child welfare worker–client dyads was interviewed to determine worker attributes and actions that were key to the development of good working relationships. Innovative features of the research design, such as a multiple interview format with two individual and one joint interview for each worker and client (five interviews per dyad) and opportunities for the worker and client in each dyad to reflect on and respond to the other’s interview transcripts, produced rich data and revealed high levels of congruency among workers, clients and researchers about worker relationship competencies. Two categories of themes that emerged from the qualitative analysis are discussed: (1) soft, mindful and judicious use of power; and (2) humanistic attitude and style that stretches traditional professional ways‐of‐being. Implications for the hiring, education and training, and supervision of child welfare workers are presented.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".