Enacting Firm, Fair and Friendly Practice: A Model for Strengths-Based Child Protection Relationships?
Bibliographic record
Abstract
Strengths-based solution-focused approaches are gaining ground in statutory child protection work, but few studies have asked front line practitioners how they navigate the complex worker-client relationships such approaches require. This paper describes one component of a mixed-methods study in a large Canadian statutory child protection agency in which 225 workers described how they applied the ideas of strengths-based practice in their daily work. Interviews with twenty-four practitioners were analysed using an interpretive description approach. Only four interviewees appeared to successfully enact a version of strengths-based practice that closely mirrored those described by key strengths-based child protection theorists and was fully congruent with their mandated role. They described navigating a shifting balance of collaboration and authority in worker-client relationships based on transparency, impartial judgement, attentiveness to the worker-client interaction and the value that clients were fellow human beings. Their accounts extend current conceptualisations of the worker-client relationship in strengths-based child protection work and are congruent with current understandings of effective mandated relationships. They provide what may be a useful model to help workers understand and navigate relationships in which they must reconcile their own authority and expertise with genuine support for the authority and expertise of their clients.
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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 | 0.019 | 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 | 0.014 | 0.069 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".