Bibliographic record
Abstract
INTRODUCTION: Reflective social work supervision is essential to professional development, building resilience and client work. However, in child protection, supervision is preoccupied with managing risk and meeting outcomes at the expense of analysis and critical reflection. Oranga Tamariki (OT), the statutory child protection organisation in Aotearoa New Zealand, has recently been scrutinised for poor supervisory practice. The authors worked alongside OT social work supervisors and supervisees to explore ways to generate resilience, learning, self-awareness and develop practices that support reflective capability and well-being in supervision. METHODS: This article presents data from the pre/post online evaluation of an action research intervention study with OT supervisors and supervisees. The aim of the online survey was to measure participants’ supervision practices, and the extent to which perceptions of confidence, reflection, professional learning and resilience improved. FINDINGS: The findings are reported from key areas within OT supervision: the frequency of supervision sessions, the functions of supervision, engagement in reflection, supervision- changing practice, resilience and longevity in social work careers and the supervision of supervisors. CONCLUSIONS: The results from the survey showed social workers had increased confidence as they built reflective capacity, resiliency and improved their supervision practice. The study identified the importance of developing learning spaces that enhance reflective supervision for supervisors and supervisees in child protection.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.122 | 0.054 |
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".