The Impact of the COVID-19 Pandemic on Social Workers at the Frontline: A Survey of Canadian Social Workers
Bibliographic record
Abstract
Abstract Social workers are facing increasingly complex client needs during the coronavirus disease of 2019 (COVID-19) pandemic. Because of the social distancing requirements of the pandemic, social workers have undergone transformative changes in practice with the rapid uptake of virtual technologies. The objective of our study was to understand the experiences of social workers during the first-wave of the COVID-19 pandemic. We conducted a cross-sectional, web-based survey, comprised of close-ended and open-ended questions. Survey participants included social workers who were the members of a provincial social work association in Ontario, Canada. With n = 2,470 participants, the response rate was close to 40 per cent. Descriptive statistics were conducted on the close-ended questions. Two open-ended questions were coded using the thematic analysis. Nine themes were identified on the impact to social worker’s employment status: increased work-load; loss of employment; redeployment to new settings; early retirement; concern for personal health and safety; social workers in private practice seeing fewer clients; personal caregiving responsibilities; limiting recent graduates’ employment potential and social workers experiencing new opportunities. There were five themes on the impact on social work practice: clients with increasing complexities; challenges with transition to virtual care; benefits with transition to virtual care; adapting in-person services and personal well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".