Integrating relational knowing and structured learning in social work placements – a framework for learning in practice
Bibliographic record
Abstract
Professional placements are integral to social work education and provide formative but variable learning opportunities for students. As social work programs expand and requirements for placements increase, settings where a social worker may not be employed are increasingly utilized, risking further variability of student experience. This paper reports on qualitative responses to two open-ended questions in a cross-sectional survey conducted with social work students from four universities on the island of Ireland. Questions included (1) what students found most helpful in assisting their learning, and (2) what would have improved their learning during placement. A six-step approach to thematic analysis was used to analyse qualitative data from 427 responses to question one, and 355 responses to question two. Four key pillars of practice learning were identified: enabling relationship(s); ‘real world’ practice opportunities; structured teaching and learning; and academic-practice alignment. Drawing on these findings, the paper presents a framework for integrated learning which promotes students’ capacity for active inquiry in practice. Linked processes of relational knowing and structured teaching and learning emerged as integral to knowledge acquisition and professional development on placement. Findings re-established the significance of the supervisory relationship and relationship-based learning.
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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 | 0.023 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.009 | 0.088 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".