Lessons learned from an in-house social work placement: practicum students’ views about a stress reduction peer support pilot project
Bibliographic record
Abstract
Securing social work field placements is increasingly becoming a challenge for social work education around the globe. In response to an unexpected and sudden shortage of field placements in a small primarily undergraduate university near Toronto, Canada, the authors developed an in-house Stress Reduction Peer Support (SRPS) pilot project to provide 12 third-year undergraduate students with the 240 placement hours required to complete their Bachelor of Social Work degree. In-depth interviews with eight of the 12 practicum students revealed support for the idea of the in-house SRPS placement. However, practicum students also expressed the need for improvements if such an unconventional placement were to be offered again. These included the importance of advanced planning for in-house placements, ensuring students do not have unrealistic expectations about placements, allowing students to choose whether to participate in unconventional placements, ensuring the educational content of the placement aligns with what students are learning in their courses, being clear about how each component of the placement aligns with social work anti-oppressive practice, and ensuring sufficient social work-specific supervision. The use of real-plays as opposed to role-plays was questioned, especially regarding students’ discomfort with sharing personal and sensitive information and submitting video-recordings via the Internet.
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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 | 0.029 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.007 | 0.013 |
| 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".