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Predicting Child Welfare Future Placements for Foster Youth : = An Application of Statistical Learning to Child Welfare.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Predicting Child Welfare Future Placements for Foster Youth :/
其他題名:	An Application of Statistical Learning to Child Welfare.
作者:	Benesh, Andrew S.
面頁冊數:	1 online resource (133 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
Contained By:	Dissertation Abstracts International78-10A(E).
標題:	Social work. -
電子資源:	click for full text (PQDT)
ISBN:	9781369863239

Predicting Child Welfare Future Placements for Foster Youth : = An Application of Statistical Learning to Child Welfare.
Benesh, Andrew S.

Predicting Child Welfare Future Placements for Foster Youth :An Application of Statistical Learning to Child Welfare. - 1 online resource (133 pages)

Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.

Thesis (Ph.D.)--The Florida State University, 2017.

Includes bibliographical references

PROBLEM: Limited understanding of factors that lead to placement disruption and entry into higher levels of care has been a longstanding problem in child welfare research and practice. While prior research has successfully identified some variables that are associated with placement instability, these findings are limited by methodological shortcomings and limited evidence of predictive utility.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369863239Subjects--Topical Terms:

1008643
Social work.
Index Terms--Genre/Form:

554714
Electronic books.

Predicting Child Welfare Future Placements for Foster Youth : = An Application of Statistical Learning to Child Welfare.
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245 1 0 $a Predicting Child Welfare Future Placements for Foster Youth : $b An Application of Statistical Learning to Child Welfare.
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300 $a 1 online resource (133 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-10(E), Section: A.
500 $a Adviser: Ming Cui.
502 $a Thesis (Ph.D.)--The Florida State University, 2017.
504 $a Includes bibliographical references
520 $a PROBLEM: Limited understanding of factors that lead to placement disruption and entry into higher levels of care has been a longstanding problem in child welfare research and practice. While prior research has successfully identified some variables that are associated with placement instability, these findings are limited by methodological shortcomings and limited evidence of predictive utility.
520 $a METHOD: This study attempts to use child, caseworker, and caregiver factors to predict placement type and change in level of care over an 18 month period using random forest modeling. Data from the NSCAW I LTFC sample were used to train and evaluate predictive models.
520 $a RESULTS: Models predicting placement type performed fairly, while models attempting to predict changes in level of care were unsuccessful.
520 $a CONCLUSIONS: Future research should continue to consider nonlinear methods for evaluating child welfare outcomes. Consideration of a broader range of variables, localized data, and alternative measurement approaches are suggested.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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