Reimagining the pedagogy of professional judgment and decision making in social work: teaching evidence-based practice in parallel with field practicum
Bibliographic record
Abstract
Social work practice occurs in varied practice contexts. To promote successful service user outcomes, professional judgment and decision making are key. However, services are delivered in settings that may be complicated by a myriad of factors, all of which may negatively impact professional judgment and decision making. This complexity of service delivery makes it difficult for social work education to promote the knowledge and skills required for ‘real world’ practice. Evidence-based Practice (EBP) is one route to ethical and effective service, as it is a method of promoting decision making skills. However, EBP is not taught consistently in pedagogy, conceptualization, or application. A pedagogical model for teaching social work students professional judgment and decision-making skills through EBP is presented in this manuscript. EBP is the equal consideration of four key factors—case context, service user values and preferences, worker and organizational biases and experiences, research—which are assessed and integrated via critical thinking. This model presents teaching EBP within a seminar in parallel with field placements, where students are required to work through a case from their field placement. This model provides the time necessary for students to engage meaningfully in the process of learning and applying EBP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".