Making strengths-based practice work in child protection : frontline perspectives
Bibliographic record
Abstract
Strengths-based practice has been widely promoted as a preferred approach for statutory child protection work, but its complexity and inconsistent implementation suggest that it may be hard to do. This pragmatic mixed methods study asked frontline workers whether and how they applied strengths-based and solution-focussed ideas in their daily child protection practice and what supported and impeded their efforts. Via 26 semi-structured interviews and an online survey, 225 child protection workers from the Ministry of Family Development in British Columbia, Canada, gave their views. The study found nearly all workers to be positive about strengths-based practice but 70% had implicit rules about the clients and situations for which strengths-based solution-focussed work was not appropriate or possible. Five definitions of strengths-based practice were identified from their descriptions of the approach, each linked to particular challenges and supports. The most applicable definition, 'Enacting firm, fair and friendly practice', illustrates a way for practitioners to navigate the strengths-based child protection relationship while managing the risk and authority inherent to their role. It involves maintaining a shifting balance between inviting maximum client collaboration and using authority purposefully and suggests the importance of judging impartially, being transparent, attending to the worker-client interaction and seeing clients as fellow human beings. It may support workers to navigate a developmental progression from occasionally doing strengths-based practice to identifying as strengths-based practitioners. The study highlights the importance of developing organisational capacity to provide the time, resources and culture necessary for strengths-based practice. It suggests that 'fearlessness', emotional self-regulation and comfort with mandated authority support strengths-based practice and recommends greater emphasis be placed on these qualities in child welfare education. Other recommendations include adopting a system-wide child protection-applicable definition of strengths-based practice like 'Enacting firm, fair and friendly practice', implementing 'strengths-based management' and providing ongoing opportunities for practitioner reflection and education.
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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 | 0.036 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.043 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".