Bibliographic record
Abstract
Introduction ‘Disability’ is a wide-ranging concept which generates varying interpretations. In countries such as the UK, the US, Canada and Australia, it has been used to describe and also to challenge the socially created disadvantage and discrimination that people with physical or emotional impairments or illnesses face. In parts of Africa, the Indian subcontinent and Asia, situations of extreme poverty and the lack of adequate resources make daily survival the prime consideration. In this chapter, I will draw from my practice and research experience in the field of disability to examine globalising trends and health inequalities, using heart disease and Type 2 diabetes as core examples, to explore how disabling social, economic and political responses compound inequalities, and to start to map out the parameters for adaptable and flexible social work responses. Globalising trends and health inequalities Recent decades have seen some major improvements in population health across the globe. In developed and developing nations, increased income and wealth has resulted in enhanced underlying social conditions for health while clean water programmes, advanced pollution control measures, health education programmes, targeted screening procedures, vaccinations and increasing access to medical treatments, have made a significant contribution. The value of concerted international action by governments and non-governmental organisations to combat long-term and life-threatening illnesses has also been increasingly recognised, not only in response to the fear of global pandemics such as bird flu (Shisana, 2005). The World Health Organization (WHO), United Nations (UN) and other international bodies have also coordinated action on global diseases such as TB (WHO, 2006) and HIV/AIDS (WHO, 2008a). In the case of some diseases, such as smallpox, such co-coordinated action has resulted in the effective eradication of the condition (WHO, 2008b). However, as this book repeatedly testifies, gross socially created inequalities in health outcomes remain between and within countries. Despite repeated international agreements to address the social determinants of health, for example, through the Millennium Development Goals or the WHO Commission on the Social Determinants of Health, actions remain far behind the rhetoric. Meanwhile, continuing military conflict, such as that in Iraq, Afghanistan and in African nations such as Sierra Leone or Darfur, exacerbates the proliferation of the arms trade, including the use of landmines, and results in both directly caused physical and emotional impairments and illnesses and indirect costs to health (Salahaddin, 2006).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".