Applying a transformative consumer research lens to understanding and alleviating poverty

Increasing attention to global poverty and the development of market-based solutions for poverty alleviation continues to motivate a broad array of academicians and practitioners to better understand the lives of the poor. Yet, the robust perspectives residing within consumer research remain to a large degree under-utilized in these pursuits. This paper articulates how applying a transformative consumer research (TCR) lens to poverty and its alleviation can generate productive insights with potential to positively transform the well-being of poor consumers

Can health workers reliably assess their own work? A test-retest study of bias among data collectors conducting a Lot Quality Assurance Sampling survey in Uganda.

BACKGROUND Lot Quality Assurance Sampling (LQAS) is a classification method that enables local health staff to assess health programmes for which they are responsible. While LQAS has been favourably reviewed by the World Bank and World Health Organization (WHO), questions remain about whether using local health staff as data collectors can lead to biased data. METHODS In this test-retest research, Pallisa Health District in Uganda is subdivided into four administrative units called supervision areas (SA). Data collectors from each SA conducted an LQAS survey. A week later, the data collectors were swapped to a different SA, outside their area of responsibility, to repeat the LQAS survey with the same respondents. The two data sets were analysed for agreement using Cohens' kappa coefficient and disagreements were analysed. RESULTS Kappa values ranged from 0.19 to 0.97. On average, there was a moderate degree of agreement for knowledge indicators and a substantial level for practice indicators. Respondents were found to be systematically more knowledgeable on retest indicating bias favouring the retest, although no evidence of bias was found for practices indicators. CONCLUSIONS In this initial study, using local health care providers to collect data did not bias data collection. The bias observed in the knowledge indicators is most likely due to the 'practice effect', whereby respondents increased their knowledge as a result of completing the first survey, as no corresponding effect was seen in the practices indicators