Critical social work and competency practice: a proposal to bridge theory and practice in the classroom
Bibliographic record
Abstract
Social work education in North America is increasingly focused on competency criteria and micro skills training for future practitioners. Market forces are transforming the nature of social work practice in Canada, and social work regulators are concerned about the lack of evidence-based competencies in social work education. Since the Controlled Act of Psychotherapy was proclaimed in 2017, social work practitioners are in a position to offer psychotherapy services; as a result, universities are under greater pressure to shift to competency-based learning that meets the requirements of a regulated profession. This has raised concerns about the narrowing focus on critical social work theory in preparing students for practice. The divergence between anti-oppressive and direct practice schools is widening with the result that many students have difficulty bridging the gap between critical theory and competency-based practice. This paper attempts to integrate both traditions by offering students a course that directly links critical analysis of structural barriers and client centered interventions. The course is part of a developing critical competency curriculum that focuses on methods of facilitating empowerment and change in the helping process by articulating key relational components between service user and practitioner from a critical-competency perspective.
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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 | 0.043 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.060 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".