#socialwork: An International Study Examining Social Workers’ Use of Information and Communication Technology
Bibliographic record
Abstract
Abstract Information and Communication Technologies (ICTs) permeated social work practice before coronavirus disease 2019 (COVID-19). In addition to ICT-based formal services (e.g. e-counselling), social workers used ICTs informally as an adjunct to face-to-face practice. Building on our previous research, our cross-sectional online survey examined social workers’ informal use of ICTs in four countries: Canada, the USA, Israel and the UK. The survey was administered through Qualtrics software among social workers across Canada (n = 2,609), the USA (n = 1,225), Israel (n = 386) and the UK (n = 134), and analysed using IBM SPSS Statistics version 26. The findings substantiate the ubiquitous use of informal ICTs in social work practice, as an adjunct to face-to-face treatment, across the four countries. Given the current, unprecedented context of COVID-19, we discuss the meaning of our findings related to access, ethical considerations (e.g. professional boundaries) and supervision in the context of restricted face-to-face practice. We discuss the implications for social work practice, education and research, and conclude that in the COVID-19 context, there is an even greater need for research, clinical discussion, supervision and policy on informal ICT use in social work practice.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".