Responding to COVID-19: New Trends in Social Workers’ Use of Information and Communication Technology
Bibliographic record
Abstract
Abstract COVID-19 changed the context for Information and Communication Technology (ICT) use globally. With face-to-face practice restricted, almost all communication with clients shifted to ICTs. Starting in April 2019, we conducted semi-structured interviews with social workers from four agencies serving diverse populations in a large urban centre, with the aim of exploring social workers’ informal ICT use with clients. Approximately 6 weeks after the cessation of face-to-face practice in March 2020 due to COVID-19 measures, we re-interviewed social workers (n = 11) who had participated in our study. Second interviews were based on a newly developed interview guide that explored social workers’ use of ICTs with clients in the context of COVID-19. Analysis of transcribed interviews revealed that the context of COVID-19 had generated two main themes. One, a paradigm shift for social workers was characterized by (a) diverse ICT options, (b) client-driven approach, and (c) necessary creativity. The second theme entails the impact of this transition which involved (a) greater awareness of clients’ degree of access, (b) confidentiality and privacy, and (c) professional boundaries. We discuss these themes and sub-themes and present implications for practice and research in a Post-COVID-19 world.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| 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".