The Impact of COVID-19 on Social Work Practice in Canada
Bibliographic record
Abstract
Social workers involved in child maltreatment investigations faced considerable challenges during the COVID-19 pandemic. Interactions with children and families carried new restrictions and risks, which resulted in changes in practice. We conducted a two-phase, mixed-methods study which examined the impact of the COVID-19 pandemic on social workers who work with maltreated children from both urban and rural areas across Canada. More specifically, we examined changes in service delivery, as well as perceptions of safety, stress, worry, and how support differed between urban and rural social workers. Fifty social workers (62% urban, 38% rural) responded to the Phase 1 survey, disseminated in May 2020, with 34 (76% urban, 24% rural) responding to the Phase 2 survey in November 2020. Quantitative and qualitative data revealed that rural social workers reported more worry, stress and a greater need for mental health support, in addition to receiving less support than urban social workers during the first wave of COVID-19 cases. However, during the second wave of cases, urban social workers reported more stress, a greater need for mental health support, and receiving less support than rural social workers. Additional research is needed to further uncover the nature of the differences between rural and urban social workers, and to identify the prolonged effects of the COVID-19 pandemic on social workers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".