Social Work Student Experiences of Completing Internships in Canada During COVID-19: Application of a Remote Learning Plan
Bibliographic record
Abstract
On March 11, 2020, the World Health Organization declared a global pandemic as a result of the spread of the COVID-19 coronavirus, a severe acute respiratory syndrome. Public health authorities throughout Canada were emphasizing early detection, physical distancing, hand washing, sheltering in place through household and self-isolation, and the closing of schools and businesses. For universities it meant the cancelation of classes and an immediate move to virtual or online learning to finish semesters, some of which were within weeks of completion, others that were just beginning. For the School of Social Work at King’s University College, London, Ontario, Canada, the restrictions and limitations imposed by the pandemic had far reaching implications that went beyond a disruption in classroom instruction and also meant terminating or suspending field practicums. Rather than having student learning succumb to the virus, the School of Social Work instituted a creative solution that involved the students developing Remote Learning Plans with the support of their Field Instructors and Faculty Consultants who would serve to minimize the disruption to the students’ learning. This study explores the student experience in moving to remote learning plans – specifically what challenges, changes, and opportunities for growth it provided.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".