Objectives: To determine the awareness and practices of medical and dental doctors in detecting and reporting suspected cases of child physical abuse. Methods: A cross-sectional study was done from November 2017 to June 2018 among medical and dental doctors practising in public and private hospitals across Pakistan. Using convienence sampling technique a structured questionnaire was administered. The questionnaire addresses knowledge of the social indicators of child physical abuse, response to child physical abuse, and actions taken by doctors when they believe a child abuse case has been decided. Descriptive analysis was done, and Chi-square test was used for the association of knowledge about child physical abuse and sex. P-value < 0.05 was considered as statistically significant. Results: Out of total 575 doctors, 347 (60.3%) were males and 446 (77.6%) work in private hospitals. The majority of doctors 384 (66.8%) had <10 years of experience and only 99(17.2%) had received formal training of child abuse. A fifth of doctors agreed to tell someone immediately after being physically abused for social indicators of child physical abuse and considered statistically significant between the sexes (P<0.05). Most doctors 450(78.3%) strongly agreed on the value of identifying and documenting child physical abuse while 563(97.9%) doctors did not take any action to suspect a child abuse case. Conclusion: The study revealed sufficient knowledge among doctors about child physical abuse. Although the doctors had a positive attitude regarding child physical abuse, a large proportion remain silent on its suspicion. Keywords: Child, Physical abuse, Physicians, Continuous...
With the ever-growing volume of online news feeds, event-based organization of news articles has many practical applications including better information navigation and the ability to view and analyze events as they develop. Automatically tracking the evolution of events in large news corpora still remains a challenging task, and the existing techniques for Event Detection and Tracking do not place a particular focus on tracking events in very large and constantly updating news feeds. Here, we propose a new method for robust and efficient event detection and tracking, which we call RevDet algorithm. RevDet adopts an iterative clustering approach for tracking events. Even though many events continue to develop for many days or even months, RevDet is able to detect and track those events while utilizing only a constant amount of space on main memory. We also devise a redundancy removal strategy which effectively eliminates duplicate news articles and substantially reduces the size of data. We construct a large, comprehensive new ground truth dataset specifically for event detection and tracking approaches by augmenting two existing datasets: w2e and GDELT. We implement RevDet algorithm and evaluate its performance on the ground truth event chains. We discover that our algorithm is able to accurately recover event chains in the ground-truth dataset. We also compare the memory efficiency of our algorithm with the standard single pass clustering approach, and demonstrate the appropriateness of our algorithm for event detection and tracking task in large news feeds.
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