Rethinking right and wrong: social work, COVID-19 and the crisis of ethics
Bibliographic record
Abstract
This epidemic is not just about people who tested positive but also about people whose life ended and deteriorated as an indirect result of the inhuman management of this epidemic. (Hospital social worker, Canada) One of the challenges I am currently facing is being more human than professional. (Medical social worker, Colombia) We felt sad, stressed, and sometimes exhausted … we did not have time to think and reflect. I felt like I was at a war. (Community social worker, China) This chapter highlights the ethical implications of COVID-19, seeing it as a crisis of social justice for social work. Drawing on responses to an international survey, it illustrates how social workers had to rethink the meaning of ethical practice in real time, balancing privacy against health risks, empathy against efficiency and rule-following against being human. It argues for framing social work ethics with values of radical social justice and empathic solidarity at its heart. The continuing impact of COVID-19 is as much a crisis of social justice, and hence of ethics, as it is of health or the economy. As such, it calls for a spirited social work response, which, as the title of this chapter suggests, calls into question ‘business as usual’. COVID-19 creates huge challenges for the profession, as social workers, social work organisations and governments work out what needs to change and how, both short and long term. These questions are not only political and practical, but also fundamentally ethical.
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 | 0.047 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.051 | 0.181 |
| Scholarly communication | 0.033 | 0.026 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.017 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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".