Using creativity, co-production and the common third in a communication skills module to identify and mend gaps between the stakeholders of social work education
Bibliographic record
Abstract
The involvement of stakeholders – academics, service users and carers, students and practitioners – is thought to improve the quality of social work education, although few approaches and strategies for achieving this have been articulated. Even service-user and carer involvement, which is firmly embedded within social work courses in the UK, would benefit from being better theorised and researched. This paper considers how creativity, co-production and the common third helped social work academics from an English university and service users and carers from a local user-led organisation to design, deliver and evaluate a communication skills module for social work students. In spite of some challenges, effective and supportive relationships have developed, with a range of benefits becoming increasingly apparent. However, the strengths of this partnership highlighted gaps in the relationships with other stakeholders. In a conscious effort to overcome paternalistic traditions of transmission-oriented teaching, some gap-mending strategies were developed to involve students in the module’s design, delivery and evaluation. It is proposed that social pedagogy, with its focus on social inclusion and social justice, might help fulfil a current aim of British higher education, to work with students as partners and increase meaningful involvement and collaboration.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".