Preventive social work intervention and health promotion
Bibliographic record
Abstract
Introduction One dimension of social work's contribution to tackling health inequalities is to focus on preventive interventions which both build and build on the resources of disadvantaged local communities to benefit their health. This chapter offers three contrasting examples of this kind of intervention drawn from very different social contexts in Australia, China and Hong Kong. These show how social workers, acting alongside public health and other professionals, can develop and support grassroots action for better health by members of geographical communities and communities of interest in the face of the rapid social, economic and environmental changes accelerated by globalisation. Section 11.1 analyses the context in which Indigenous Australians experience an average life expectancy of some 17 years less than that of the majority population (CSDH, 2008). A substantial distrust of social workers and health professionals has resulted from their involvement in oppressive and discriminatory social policies including the removal of children. However, this analysis of a group work intervention based on a transdisciplinary, Family Wellbeing empowerment programme shows that social workers can help to strengthen Indigenous people's own sense of control and their capacity to take prominent roles in local public policy making with the aim of reducing health damage. Section 11.2 discusses a joint Canadian–Chinese development programme, particularly focused on women's health, in rural Mongolia where one response to poverty has been a large-scale exodus of men to seek work in cities. Rooted in a training programme based on basic social work methods and values, this project recruited local women to be leaders in health education and promotion work with a number of expected and unexpected consequences. Trainers came to recognise the expertise of rural women in analysing and addressing the barriers they face to health, including the inadequacy of health care provision. This has produced a shift in the approach of the All China Women's Federation to its task of nationwide health promotion. Finally, Section 11.3 reports on an intervention to build health-related social capital in Hong Kong through the Community Investment and Inclusion Fund, with social work leaders. Although a strategic intervention led by the Fund's managers, the core principle of the model was the building of social capital through a fundamental change in welfare approach from service provision to participatory grassroots action.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 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".