This report presents the major findings of the second cycle of the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS-2003). In this initiative, data are collected every five years on child maltreatment reported to, and investigated by, child welfare agencies in Canada. The CIS is a collaborative effort of many partners: the federal, provincial and territorial governments; university-based researchers; the First Nations Child & Family Caring Society; child advocacy groups; and most important, child welfare service providers across the country.

Canadian Incidence Study of Reported Child Abuse and Neglect. 2000.

/pdf/international-covenant-on-economic-social-and-cultural-3rlojvar7i.pdf

International Covenant on Economic, Social and Cultural Rights

https://dspace.stir.ac.uk/bitstream/1893/24541/1/Inverse_Intervention_rates%20Article_V1_final_2.pdf

Exploring inequities in child welfare and child protection services: Explaining the ‘inverse intervention law’

Importance Nearly 6 million children are reported as allegedly experiencing abuse or neglect in the US annually. Child protection agencies are increasingly turning to automated predictive risk models (PRMs) that mine information found in routinely collected administrative data and estimate a likelihood that an individual will experience some future adverse outcome. Objective To test if a PRM used at the time of referral for alleged maltreatment, which automatically generates a risk stratification score indicating the relative likelihood of future foster care placement, is also predictive of injury hospitalization data. Design, Setting, and Participants This retrospective cohort study based on a probabilistic association between child protection and hospital encounter data was conducted in Allegheny County, Pennsylvania, and at Children’s Hospital of Pittsburgh (Pittsburgh, Pennsylvania). Participants included children referred for alleged neglect or abuse in Allegheny County between April 1, 2010, and May 4, 2016. Exposures Risk score generated from the PRM. Main Outcomes and Measures Medical encounters (emergency department and inpatient hospitalizations) for any-cause injuries, suicide or self-inflicted harm injuries, and abuse injuries between 2002 and 2015 for children classified by the PRM to different risk levels at the time of a maltreatment referral. Cancer encounters were used as a placebo test. Results Of 47 305 participants, 23 601 (49.9%) were girls, the mean (SD) age at referral was 8 (5.7) years, 28 211 (59.6%) were black, and 19 094 (40.4%) were nonblack. Children who scored in the highest 5% risk group by the PRM were more likely to have a medical encounter for an injury during the follow-up period than low-risk children (ie, those in the bottom 50% of risk). Specifically, among children referred for maltreatment and classified as highest risk, the rate of experiencing an any-cause injury encounter was 14.5 (95% CI, 13.1-15.9) per 1000 compared with children who scored as low risk who had an any-cause injury encounter rate of 4.9 (95% CI, 4.7-5.2) per 1000. For abuse-associated injury encounters, the rate for high-risk children was 2.0 (95% CI, 1.5-2.6) per 1000 and that of low-risk children was 0.2 (95% CI, 0.2-0.3) per 1000; for suicide and self-harm, the high-risk encounter rate was 1.0 (95% CI, 0.6-1.4) per 1000 and that of low-risk children was 0.1 (95% CI, 0.1-0.1) per 1000. There was no association between risk scores and cancer encounters. Conclusions and Relevance Findings confirm that children reported for having experienced alleged maltreatment and classified by a PRM tool to be at high risk of foster care placement are also at increased risk of emergency department and in-patient hospitalizations for injuries.

Hospital Injury Encounters of Children Identified by a Predictive Risk Model for Screening Child Maltreatment Referrals: Evidence From the Allegheny Family Screening Tool

There is a replication crisis in science, to which psychological research has not been immune: Many effects have proven uncomfortably difficult to reproduce. Although the reli- ability of data is a serious concern, we argue that there is a deeper and more insidious problem in the field: the persistent and dramatic misinterpretation of empirical results that replicate eas- ily and consistently. Using a series of four highly studied "text- book" examples from different research domains (eyewitness memory, deductive reasoning, social psychology, and child wel- fare), we show how simple unrecognized incompatibilities among dependent measures, analysis tools, and the properties of data can lead to fundamental interpretive errors. These errors, which are not reduced by additional data collection, may lead to misguided research efforts and policy recommendations. We conclude with a set of recommended strategies and research tools to reduce the probability of these persistent and largely unrecog- nized errors. The use of receiver operating characteristic (ROC) curves is highlighted as one such recommendation.

https://link.springer.com/content/pdf/10.3758%2Fs13423-014-0759-2.pdf

When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions.

Reframing child protection: A response to a constant crisis of confidence in child protection ☆

Child welfare data collected for administrative purposes are often used as a source of information for understanding the population impact of child abuse and neglect (CA/N). This study used administrative data linked at the individual level for a cohort of Aotearoa New Zealand (NZ) children to follow and extend a model developed by Drake et al. In this investigation, we aimed to build an understanding of the high representation of indigenous NZ children in administratively sourced measures of CA/N. Variation in rate ratios (RRs) within infant mortality and birth outcomes considered as possible proxies for actual CA/N RRs leaves open a range of interpretations. Our findings indicate that a more nuanced interpretation of the overrepresentation of indigenous children in administratively recorded maltreatment statistics is required. Rather than considering risk and bias as competing explanations, we suggest an acknowledgment of the impact of colonization and the existence of systemic bias generating increased risk as key drivers. As linked administrative data are increasingly used for research and evaluation, and considered for use in supporting decision making, there is a need for a deeper understanding of the drivers of administratively recorded CA/N in order to effectively address the needs of indigenous populations. Language: en

Understanding overrepresentation of indigenous children in child welfare data: an application of the Drake risk and bias models

Predictive Modeling: Potential Application in Prevention Services

EPH176 What Macro-Level Social Determinants Are Associated with Mental Health Outcomes in Disability-Adjusted Life Year (DALY) for the Population Living in the Organisations for Economic Co-Operation and Development (OECD) Nations?

Rissa Ota

Papers

Reframing child protection: A response to a constant crisis of confidence in child protection ☆

Understanding overrepresentation of indigenous children in child welfare data: an application of the Drake risk and bias models

Predictive Modeling: Potential Application in Prevention Services

EPH176 What Macro-Level Social Determinants Are Associated with Mental Health Outcomes in Disability-Adjusted Life Year (DALY) for the Population Living in the Organisations for Economic Co-Operation and Development (OECD) Nations?