Timebanking and the co‐production of preventive social care with adults; what can we learn from the challenges of implementing person‐to‐person timebanks in England?

This paper explores the potential contribution of timebanking, an innovative volunteering scheme, to the co-production of preventive social care with adults in England. Interest in volunteering in social care has increased as one proposed solution to the international crisis of a rising demand for services in juxtaposition with decreased resources. Volunteering has been particularly promoted in preventive services that prevent or delay care needs arising. Despite sustained interest in volunteering and co-production in social care, little is known about how theory translates into practice. Reporting implementation data from a Realistic Evaluation of six case studies in England, this paper explores one volunteering scheme, timebanking. The research explores how timebanks were working, what contribution they can make to adult social care, and whether they are an example of co-production. Data collected included interviews, focus groups or open question responses on surveys from 84 timebank members, and semi-structured interviews with 13 timebank staff. Each timebank was visited at least twice, and all timebank activity was analysed for a period of 12 months. Data were triangulated to improve reliability. The research found that in practice, timebanks were not working as described in theory, there were small numbers of person-to-person exchanges and some timebanks had abandoned this exchange model. Timebanks faced significant implementation challenges including managing risk and safeguarding and the associated bureaucracy, a paternalistic professional culture and the complexity of the timebank mechanism which required adequate resources. Lessons for timebanks are identified, as well as transferable lessons about co-production and volunteering in social care if such schemes are to be successful in the future

Expansion of Dental Care for Low‐Income Children Through a Mobile Services Program

BackgroundAlthough access to dental care has improved over time, many children still face difficulty in obtaining services. One strategy to increase access is through mobile dental services, often in collaboration with schools, Head Start programs, and school-based health centers. This study evaluates a large mobile dental care program based in Minnesota.MethodsThematic analysis of interview data collected during a 2-day site visit and multivariate regression analysis of electronic records of patients (adults and children) that received care from 2000 through 2015, representing 84,279 unique patients.ResultsThe number of patients increased from 5558 in 2000 to 13,863 in 2015. There was a decline in the share of preventive procedures over this period, from 45.7% to 29.4%, and an increase in the share of patients seen at fixed sites. The interview data revealed that program growth relied on relationships with school leaders, expanded scope of practice for dental assistants and dental therapists, and high Medicaid reimbursement.ConclusionsMobile dental care programs can increase both preventive and restorative dental care for individuals who otherwise would not easily access oral health care services; mobile dental programs could be an option in many other communities and schools

Modeling health insurance expansions: Effects of alternate approaches

Estimates of the costs and consequences of many types of public policy proposals play an important role in the development and adoption of particular policy programs. Estimates of the same, or similar, policies that employ different modeling approaches can yield widely divergent results. Such divergence often undermines effective policymaking. These problems are particularly prominent for health insurance expansion programs. Concern focuses on predictions of the numbers of individuals who will be insured and the costs of the proposals. Several different simulation-modeling approaches are used to predict these effects, making the predictions difficult to compare. This paper categorizes and describes the different approaches used; explains the conceptual and theoretical relationships between the methods; demonstrates empirically an example of the (quite restrictive) conditions under which all approaches can yield quantitatively identical predictions; and empirically demonstrates conditions under which the approaches diverge and the quantitative extent of that divergence. All modeling approaches implicitly make assumptions about functional form that impose restrictions on unobservable heterogeneity. Those assumptions can dramatically affect the quantitative predictions made. © 2004 by the Association for Public Policy Analysis and Management.