Assessing antenatal depression in primary care with the PHQ-2 and PHQ-9: Can It Be carried Out by auxiliary nurse midwife (ANM)?

Antenatal Depression (AD) is prevalent in India but remains unidentified and untreated in primary care settings. The Auxiliary Nursing Midwife (ANM), a non-specialist health care worker at antenatal care (ANC) clinic or at a primary care centre can improve the detection of women with AD. In this study, we aimed to estimate the inter-rater reliability of the Patient Health Questionnaire (PHQ-9) and PHQ-2, administered by ANMs, with mental health professionals, that is, Clinical Psychologists(CP). The prospective, blind, observational study was conducted in an antenatal clinic of tertiary care rural hospital of India. PHQ-2 and PHQ-9(Marathi) valid and translated versions were administered to consecutive antenatal women (n = 108) aged 18-45 years by two raters (CP, ANM) independently. Raters and data analyst were blinded to each other's scores. Kappa Coefficient, weighted kappa, Intra-class correlation coefficients (ICC) were used to assess Inter-rater reliability. The mean age of the study participants was 25.91 ± 4.39 years. Inter-rater reliability for PHQ-2 (Screen -Positive or Negative for depression) and PHQ-9 (Severity of depression) was (k = 0.675) and (kw = 0.732) respectively, which suggested the 'substantial' agreement between ANMs and CP. Among raters, the ICC (95 % confidence interval) was 0.90 (0.85, 0.93) for the PHQ-9 score and for the PHQ-2 was 0.873 (0.819-0.911), which was suggestive of excellent and good reliability respectively. The 'substantial' agreement and 'good to excellent' inter-rater reliability among ANM and CP suggest that these two scales can be used to screen and assess the severity of AD by either qualified or minimally trained, community health workers.

Thrust and torque force analysis in the drilling of aramid fibre-reinforced composite laminates using RSM and MLPNN-GA.

Aramid Fibre Reinforced Plastic composites are difficult to be drilled due to anisotropic material properties. Currently, soft computing techniques are used as alternatives to conventional mathematical models, which is robust and can deal with inaccuracy and uncertainty. In this paper, drilling of Aramid Fibre Reinforced Plastics (AFRPs) was carried out using Taguchi L54 experimental layout. Drilling tool used in this experiment was solid carbide. The purpose of this study was to find optimum combination of drilling parameters to obtain minimum thrust and torque force to reduce the delamination. Also, this paper proposed a prediction model of Multilayer Perception Neural Network optimized by Genetic Algorithm (MLPNN-GA). Moreover, RSM technique was used to evaluate the influence of process parameters (spindle speed, feed rate, drill point angle and drill diameter on thrust force and torque. The prediction capability of both RSM and MLPNN-GA was compared with Response optimizer for thrust force and torque. The investigation demonstrated that drill point angle is the primary factor affecting thrust force and drill diameter influences the torque force on the drill bit. Overall, this study recommends the use of high speed and low feed combination and drill point angles of 90°-118° to reduce the delamination of the materials in the drilling of AFRP composites.