'I thought they would just let me do my job': Work-based practica in social work and welfare
Bibliographic record
Abstract
Social work and welfare education courses are underpinned by concepts such as equity, disadvantage and access as the guiding principles of practice with client groups. However, the ‘clients’ of the university, the students, are often disadvantaged in the current economic climate as they struggle to find the work/life balance with studying. In addition, many students who also currently work in the welfare field feel further disadvantaged as the knowledge and skills they bring to the course often go unrecognised. At the same time, university educators often struggle to locate sufficient numbers of stimulating learning opportunities for practica. We wondered if the time was right to explore work based practica (WBP) as a way of addressing these issues. And we wondered what common practica was across our networks with regard to WBP. This article presents the findings of a three-stage project on the current practices, concerns, benefits and disadvantages of WBP (that is practica in a student’s place of employment) in social work and welfare education in Australia, New Zealand and Canada. Recommendations are presented so that WBP can be a more educationally sound option
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".