Pathways to Service Receipt: Modeling Parent Help-Seeking for Childhood Mental Health Problems

Understanding parent appraisals of child behavior problems and parental help-seeking can reduce unmet mental health needs. Research has examined individual contributors to help-seeking and service receipt, but use of structural equation modeling (SEM) is rare. SEM was used to examine parents’ appraisal of child behavior, thoughts about seeking help, and receipt of professional services in a diverse, urban sample (N = 189) recruited from women infant and children offices. Parents of children 11–60 months completed questionnaires about child behavior and development, parent well-being, help-seeking experiences, and service receipt. Child internalizing, externalizing, and dysregulation problems, language delay, and parent worry about child behavior loaded onto parent appraisal of child behavior. Parent stress and depression were positively associated with parent appraisal (and help-seeking). Parent appraisal and help-seeking were similar across child sex and age. In a final model, parent appraisals were significantly associated with parent thoughts about seeking help, which was significantly associated with service receipt

Regulation, Competition, and Information

You know it is very hard after the Governor, State Bank, to make a presentation but I will try to do it in a very mundane way. You know the Regulatory Bodies specially in the Economic Sector in recent times. There has been a sort of resurgence, leaving aside the regulation of the financial sector, which has been doing very well. Our old memory of regulation is not so pleasant. Long ago, there used to be a transport Authority which used to dole out “Route Permits” as political favours, and there was you know fixation of Bus Fares not always based on economic considerations but based on arbitrariness. But luckily we have learnt a lot. First, we learnt that it is good to deregulate and I think the primary purpose of the present resurgence is to deregulate. You have a regulatory body to deregulate. Secondly as the finance Minister said yesterday himself that this is a new paradigm. The regulation now has a major ingredient of a development role and in Pakistan with the combination of licencing as necessary part of regulation, you are very effective in that role and it also genuinely provides an opportunity for a one window type of operation where you give a permission and you facilitate the type approvals and then you help them dealing with the local agencies. Although you have brief period for evaluation but the preliminary perception is that they are fairing better than our Industrial Development Corporations which were given the role to promote the Private Sector. Now in this regulatory field, the new entrant is the Pakistan Electronic Media Regulatory Authority. I will present you some salient features how it works. I am not raising any issues as such like an economist would do but my presentation will be more informatic and tell you that in the new Regulatory regime in Pakistan, where is the stress now. Focus on professionalism, transparency and community participation. I will use the slides.

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page