How do child welfare supervisors approach ethical dilemmas in their practice
Bibliographic record
Abstract
Although there is extensive literature on supervision in the human services, there is limited research specific to the stories from supervisors in child welfare, in particular in Canada. This inquiry sought to understand how child welfare supervisors navigated through ethical dilemmas in their practice and how their approach influenced decision making. In addition, specific attention was paid on whether these practitioners used critical reflection in their approach to decision making. Findings indicated that these child welfare supervisors relied primarily on their personal moral framework. They encountered frequent dilemmas in highly complex work environments. Further, they endured ethical tensions as a result of not being able to enact their ethics amid work place barriers. These ongoing tensions often resulted in leaving these supervisors depleted emotionally and physically. Critical reflection in action was used in some cases when examining the context of the family in the process of ethical decision making. As with recent studies, this inquiry found that child welfare supervisors often stepped away from reflection in action for self-preservation and relied more heavily on reflection on action. Implications for future studies and recommendations for child welfare practice are discussed.
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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 | 0.021 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".