Disproportionality at the "front end" of the child welfare services system: an analysis of rates of referrals, "hits", "misses", and "false alarms
Journal of health and human services administration, 2010
Data from NIS-4, NCANDS, and the State of California were used to analyze the front end of the ch... more Data from NIS-4, NCANDS, and the State of California were used to analyze the front end of the child welfare services system--the referral and substantiation components--in terms of the system's ability to diagnose or detect instances of child maltreatment. The analyses show that Blacks are disproportionately represented in rates of referral into the system. Moreover, the analyses demonstrate that the system is less accurate for Blacks than for other racial or ethnic groups. There is a higher rate of false positives (or "false alarms") for Blacks than for other groups--that is, referrals leading to unsubstantiated findings. There is also a higher rate of false negatives (or "misses") for Blacks than for other groups--that is, children for whom no referral was made but who are in fact neglected or abused. The rate of true positives (or "hits")--children for whom a referral has been made and for whom that allegation has been substantiated--is generall...
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Papers by Jeryl Mumpower
adult respondents for five psychometric variables – Dread, Scientists’ Level of
Understanding, Public’s Level of Understanding, Number Affected, and Likelihood
– for six threats (sea-level rise, increased flooding, and four others) associated
with climate change. Respondents also rated perceived risk and indicated
the resource level that they believed should be invested in management programs
for each threat. Responses did not vary significantly across the six threats, so
they were combined. The survey collected standard demographic information, as
well as measuring climate change knowledge and environmental values (New
Ecological Paradigm, NEP). Psychometric variables predicted perceived risk
extremely well (R = .890, p < .001); all five psychometric variables were
significant predictors. The results were generally consistent with previous
research except that Scientists’ Level of Understanding was a positive, rather
than negative, predictor of perceived risk. Jointly the demographic variables,
knowledge, and environmental values significantly predicted perceived risk
(R = .504, p < .001). Consistent with previous research, significant positive predictors
were age, Democratic Party Identification, and NEP score; significant
negative predictors were male gender and White ethnicity. When demographic
variables, knowledge, and environmental values were added to psychometric
ones, only the psychometric variables were statistically significant predictors. Perceived
risk strongly predicted resource level (r = .772, p < .001). Adding demographic,
knowledge, and environmental value variables to perceived risk as
predictors of resource level did not appreciably increase overall predictive ability
(r = .790, p < .001), although White ethnicity emerged as a significant negative
predictor and religiosity, Democratic Party Identification, Liberal Political Ideology,
and NEP score were significant positive predictors. The results demonstrate
that risk perceptions of climate change and policy preferences among climate
change management options are highly predictable as a function of demographic,
knowledge, environmental values, and psychometric variables. Among these, psychometric
variables were found to be the strongest predictors.