In search of better opportunities: transnational social workers in the UK navigating the maze of global and social mobility
Bibliographic record
Abstract
Introduction There is growing evidence that transnational social workers (TSWs) contribute significantly to the national workforce of many developed countries, including Canada (Pullen Sansfaçon et al, 2012), England (Hussein et al, 2011), Ireland (Walsh et al, 2010) and New Zealand (Bartley et al, 2012). These transnational movements occur within a set of constraints at different stages, from application, qualifications recognition and securing jobs, to practising in a new environment. Some of these difficulties might arise from how social work practice has evolved as a profession within different national and local contexts, as well as how it connects to wider policies and national priorities. Others may relate to international agreements and processes of qualifications and experience recognition. Thus, different TSWs are faced by a multitude of challenges and hurdles, some of which are similar to professionals from other domains, such as medicine or engineering; yet, others are specific to the nature of social work itself. These layered challenges are observed by, and impact on, TSWs themselves, both at individual and professional levels, as well as in relation to their new context of practice in the destination countries. Aims and methods This chapter aims to discuss, based on empirical research, the various challenges and opportunities when TSWs engage in British social work practice. These are identified through the perspective of different actors, including TSWs themselves, their managers and colleagues. The analysis utilises data from different sources and studies. First, it explores trends in the levels and profile of non-UK-qualified social workers registered in England through interrogating data held by the previous and current social work regulators in England, the General Social Care Council (GSCC) and the Health and Care Professions Council (HCPC). It then draws on rich qualitative and quantitative data obtained through interviews, focus group discussions and national surveys with different stakeholders (see Hussein et al, 2013; Hussein, 2014). Data sources include: workforce records (GSCC 2003–12 and HCPC 2012–15); online surveys of non-UK-qualified TSWs ( n = 101 in 2010 and n = 32 in 2014); interviews ( n = 18) and two focus group discussions ( n = 7) with TSWs; and interviews with British managers and social workers ( n = 6) and service users ( n = 35).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".