Provider imposed restrictions to clients’ access to family planning in urban Uttar Pradesh, India: a mixed methods study

BACKGROUND: Medical barriers refer to unnecessary policies or procedures imposed by health care providers that are not necessarily medically advised; these restrictions impede clients’ access to family planning (FP). This mixed methods study investigates provider imposed barriers to provision of FP using recent quantitative and qualitative data from urban Uttar Pradesh, India. METHODS: Baseline quantitative data were collected in six cities in Uttar Pradesh, India from service delivery points (SDP), using facility audits, exit interviews, and provider surveys; for this study, the focus is on the provider surveys. More than 250 providers were surveyed in each city. Providers were asked about the FP methods they provide, and if they restrict clients’ access to each method based on age, parity, partner consent, or marital status. For the qualitative research, we conducted one-on-one interviews with 21 service providers in four of the six cities in Uttar Pradesh. Each interview lasted approximately 45 minutes. RESULTS: The quantitative findings show that providers restrict clients’ access to spacing and long-acting and permanent methods of FP based on age, parity, partner consent and marital status. Qualitative findings reinforce that providers, at times, make judgments about their clients’ education, FP needs and ability to understand FP options thereby imposing unnecessary barriers to FP methods. CONCLUSIONS: Provider restrictions on FP methods are common in these urban Uttar Pradesh sites. This means that women who are young, unmarried, have few or no children, do not have the support of their partner, or are less educated may not be able to access or use FP or their preferred method. These findings highlight the need for in-service training for staff, with a focus on reviewing current guidelines and eligibility criteria for provision of methods

GEML: A Grammatical Evolution, Machine Learning Approach to Multi-class Classification

In this paper, we propose a hybrid approach to solving multi-class problems which combines evolutionary computation with elements of traditional machine learning. The method, Grammatical Evolution Machine Learning (GEML) adapts machine learning concepts from decision tree learning and clustering methods and integrates these into a Grammatical Evolution framework. We investigate the effectiveness of GEML on several supervised, semi-supervised and unsupervised multi-class problems and demonstrate its competitive performance when compared with several well known machine learning algorithms. The GEML framework evolves human readable solutions which provide an explanation of the logic behind its classification decisions, offering a significant advantage over existing paradigms for unsupervised and semi-supervised learning. In addition we also examine the possibility of improving the performance of the algorithm through the application of several ensemble techniques