Social work ethics crossing multinational and interprofessional boundaries: smooth passages and bumpy rides
Bibliographic record
Abstract
Introduction In line with the aims of Part Five, this chapter focuses on professional and interprofessional ethics in the context of different countries (see Figure 1.1). The authors use a case study to highlight the part that cultural, geographical, professional and ideological factors can play when working across multinational contexts (Belgium, Canada, Germany, the Netherlands, and the US). The chapter analyses the values espoused by social work and other professions similar to social work, and emphasises their deontological nature. Social work is a profession that continually crosses boundaries. Typically these boundary crossings are along professional lines, as social work is often in host settings where other professionals dominate. At other times these boundaries are literally geographical crossings, for example across jurisdictional boundaries or catchment areas. When an explorer crosses boundaries, finding different values and belief systems is to be expected. When values and beliefs have similarities, the journey into new territory is made easier. This chapter will explore areas where interprofessional practice is bolstered by similar ethical stances across boundaries. The helping professions share many values and beliefs. However, there are also differences, making it easier for problems to occur. This chapter examines how clashing of professional values may be fuelled by organisational settings where differing interpretations of organisational policies or procedures foster disagreements over ‘best practice’. Ethical and value clashes are often not the real culprits in causing conflict. Rather, value and ethical dilemmas are blamed for problems that have their roots in structural barriers. If one embraces the understanding that professionals have shared values and beliefs, and that structural barriers are often the root of interprofessional ‘value clashes’, then creative approaches to overcoming difficulties can be found and implemented. To make this case, a brief overview of the history of social work and its boundary-crossing traditions is presented. Next some of the research on interdisciplinary practice is highlighted. Finally, a brief summary of professional values and ethics is presented before looking at the case study. History of social work Professions are shaped by the social and political realities of their time and reflect the prevailing ideologies and values of the larger society (Goldenberg, 1971). In Europe and North America, the social upheavals of the Industrial Revolution shaped the development of social welfare, social work and social work education.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".