Remote field placements during the pandemic: Lessons learned at the University of British Columbia School of Social Work
Bibliographic record
Abstract
COVID-19 and the global pandemic significantly shifted social work field education and required innovations in how practicum experiences could simultaneously meet experiential learning objectives and maintain safety. Remote practicums where students were connected to an agency/organization but completed all their learning/service delivery and supervision virtually was one of these innovations. As a quality assurance exercise, the Chair of the Field Education Program at the University of British Columbia (UBC) School of Social Work interviewed four MSW students about their experiences in remote practicums. This article describes some of the benefits and challenges outlined by the students and illustrates possible practices and policies that the UBC School of Social Work field team can make to harness the learning from these experiences.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| 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".