Instructor led rotational model: An innovative approach to social work practicums in hospital environments
Bibliographic record
Abstract
With the saturation of student practicum placements requests in urban centres, job vacancies, budgetary restraints, and social workers’ intensive workloads, field education coordination teams are increasingly challenged to find social workers who are able to supervise student practicums and secure educational partnerships with organizations willing to support their staff in doing so. This is especially true in hospital settings, where social workers are still feeling the effects of the Coronavirus (COVID-19) pandemic in their daily work lives. Hospital practicums are highly sought out by social work students and yet there are few practicum opportunities for students compared to the demand. Social workers are employed in various practice settings, including hospital social work. As social workers are Canada's largest group of mental health professionals within the hospital system, having robust and sustainable practicum opportunities is critical to the training and development of future social workers. To address these ongoing challenges in social work field education, the University of Calgary, MacEwan University, and Alberta Health Services worked together to develop and pilot a new practicum model at the Bachelor of Social Work level. This practicum model, titled the ‘Instructor Led Rotational Model’, was implemented as a pilot project during the winter 2023 semester. This article provides an overview of this initiative, including a description of innovative practice education components and discussion of the challenges in field education that the model aims to address. Considerations for revisions and future offerings of the instructor led practicum model are also identified.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".