The AFCARS Trends Chart tracks children in Foster Care from FY 2002 through the most recent year. A table of data and a graphic depiction of trends are shown for children in care on the first day of the year, entries to foster care, exits, children waiting to be adopted, children adopted, children with terminations of parental rights, and total children served in foster care.

This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year. Children in DFPS custody are those for whom a court has appointed DFPS legal responsibility through temporary or permanent managing conservatorship or other court ordered legal basis. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency. Children may have more than one disabling condition. This chart gives the count of each child with each disabling condition. Drug/Alcohol disabling condition can either be due to self-abuse or exposure to an individual with the condition. Other includes teen parent or pregnant teen. Visit dfps.texas.gov for more information about DFPS and our programs.

This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year. Children in DFPS custody are those for whom a court has appointed DFPS legal responsibility through temporary or permanent managing conservatorship or other court ordered legal basis. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency.

Children may have more than one disabling condition.

Drug/Alcohol disabling condition can either be due to self-abuse or exposure to an individual with the condition.

Other includes teen parent or pregnant teen.

Please visit dfps.state.tx.us for more information about DFPS Adoptions and all our programs.

In 1987, the National Health Interview Survey (NHIS) questionnaire included a special section that queried female respondents aged 20 through 54 about adoption. Their responses to the supplement are recorded in this dataset, along with other information about them derived from the core 1987 questionnaire. The special section on adoption asked if any children had ever been adopted, the number that were adopted, and whether these children currently lived in the household. Additional questions in the supplement inquired about the two most recent adoptions: how the adoptions were arranged, the adoptive mother's relationship to the adopted children before adoption, when and how old the adopted children were when they began living with the adoptive mother, the date of birth of the adopted children, and whether the adopted children were born in the United States. Variables from the core questionnaire include height, weight, age, race, Hispanic origin, type of living quarters, region and metropolitan status of residence, marital status, veteran status, education, family income, health status, industry, occupation, activity limitation status, medical conditions, restricted activity days in the past two weeks, bed days in the past two weeks and past 12 months, time interval since the last doctor visit, and the number of doctor visits and short-stay hospital episodes in the past two months.

Users can request data and reports related, but not limited to child abuse, neglect, foster care, and child well-being. Background The National Data Archive on Child Abuse and Neglect collects data on the well-being of children. The archive is a project of the Family Life Development Center, Department of Human Ecology at Cornell University. The archive collects data sets from the The National Survey of Child Health and Well-being, The Adoption and Foster Care Analysis Reporting System, The National Child Abuse and Neglect Data System, and other data related to child abuse, neglect, victimization, m altreatment, sexual abuse, homelessness, and safety. User functionality Users can access abstracts of data sets which discuss the time period and logistics of collecting the data. There are different requirements for accessing different data sets. All requirements are clearly outlined. All data sets must be ordered through the National Data Archive on Child Abuse and Neglect. Application materials must be mailed to the archive for access permission. Requirements for access vary by amount of personal information included in the data set. Data Notes The chief investigator, the years of data collection and a description of the data set is available on the website for every data set. The website does not convey when new data sets will be added.

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

This study presents data from two samples from the Texas Adoption Project. The main sample consists of 300 families who adopted a child from a Texas home for unwed mothers between 1963 and 1971. Included are ability and personality data from the adoptees and other children in the families, from the adoptive mothers and fathers, and from many of the birth mothers of the adopted children. The data include an initial study in the late 1970s, a ten-year follow-up of the children in the late 1980s, interviews with the parents in about half the adoptive families between 1997 and 2002, and a short mail questionnaire in 2002-2005 focused on life outcomes. The second sample consists of 220 families who adopted a child from the same institution prior to 1966. The data are comprised of item responses and scale scores from two personality inventories completed by mail in 1980 by 215 fathers, 219 mothers, 302 adopted children, and 63 biological children of the adoptive parents. The inventories were The California Psychological Inventory and the Thurstone Temperament Schedule. For depositor requirements for access to restricted data, click on 'other information' tab at the bottom of this page.

Abstract The necessary adoptions encompass cases of children over two years of age, with siblings, disabilities, chronic or interracial illness. This study aimed to present an integrative review of the necessary adoptions and how this topic has been investigated and discussed. From a search in the databases LILACS, SciELO, PePSIC and PsycINFO (2019-2018), 35 studies were retrieved. The scarce international production may be related to the fact that the necessary adoptions are a category in the Brazilian reality. Most of the studies prioritised as necessary the adoption of children children over two years of age, with few studies on children with disabilities, sickness and siblings. Further research on interracial adoptions is recommended, as well as greater awareness of the candidates to adopt regarding the profile of children for adoption in host institutions, which should be accompanied by greater visibility of the topic in the scientific field.

The data in the table 'Adoptions through the courts; number of children adopted at the same time' refer to adoption applications submitted to Dutch courts. The table lists the adoptive families involved in the granted requests. A distinction is made between families who have adopted 1, 2 or more children at the same time. The data in this table concern both regular adoptions and stepparent adoptions. Adoptions that fall under the Hague Adoption Convention are not included. If a child is adopted from a country that is party to this treaty, the adoption is automatically and fully recognized without the intervention of a Dutch court. Data available from: 1995. Status of the figures: All figures included in the table are final figures. When will the new numbers come out? This table will no longer be updated. In the course of 2010, a new method of observation will be adopted and a revised series will be published.

This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year and the total number of adoptive placements during the year. Children can have more than one adoptive placement. This chart includes children in DFPS custody for whom a court has appointed DFPS legal responsibility through Permanent Managing Conservatorship. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency. Visit dfps.state.tx.us for information on adoption and all DFPS programs.

The department is court ordered to complete a home investigation to determine which home is most suitable for permanency or increased visitation

Analysis of ‘Broadband Adoption and Computer Use by year, state, demographic characteristics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/720f8c4b-7a1c-415c-9297-55904ba24840 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This dataset is imported from the US Department of Commerce, National Telecommunications and Information Administration (NTIA) and its "Data Explorer" site. The underlying data comes from the US Census

1. dataset: Specifies the month and year of the survey as a string, in "Mon YYYY" format. The CPS is a monthly survey, and NTIA periodically sponsors Supplements to that survey.

2. variable: Contains the standardized name of the variable being measured. NTIA identified the availability of similar data across Supplements, and assigned variable names to ease time-series comparisons.

3. description: Provides a concise description of the variable.

4. universe: Specifies the variable representing the universe of persons or households included in the variable's statistics. The specified variable is always included in the file. The only variables lacking universes are isPerson and isHouseholder, as they are themselves the broadest universes measured in the CPS.

5. A large number of *Prop, *PropSE, *Count, and *CountSE columns comprise the remainder of the columns. For each demographic being measured (see below), four statistics are produced, including the estimated proportion of the group for which the variable is true (*Prop), the standard error of that proportion (*PropSE), the estimated number of persons or households in that group for which the variable is true (*Count), and the standard error of that count (*CountSE).

DEMOGRAPHIC CATEGORIES

1. us: The usProp, usPropSE, usCount, and usCountSE columns contain statistics about all persons and households in the universe (which represents the population of the fifty states and the District and Columbia). For example, to see how the prevelance of Internet use by Americans has changed over time, look at the usProp column for each survey's internetUser variable.

2. age: The age category is divided into five ranges: ages 3-14, 15-24, 25-44, 45-64, and 65+. The CPS only includes data on Americans ages 3 and older. Also note that household reference persons must be at least 15 years old, so the age314* columns are blank for household-based variables. Those columns are also blank for person-based variables where the universe is "isAdult" (or a sub-universe of "isAdult"), as the CPS defines adults as persons ages 15 or older. Finally, note that some variables where children are technically in the univese will show zero values for the age314* columns. This occurs in cases where a variable simply cannot be true of a child (e.g. the workInternetUser variable, as the CPS presumes children under 15 are not eligible to work), but the topic of interest is relevant to children (e.g. locations of Internet use).

3. work: Employment status is divided into "Employed," "Unemployed," and "NILF" (Not in the Labor Force). These three categories reflect the official BLS definitions used in official labor force statistics. Note that employment status is only recorded in the CPS for individuals ages 15 and older. As a result, children are excluded from the universe when calculating statistics by work status, even if they are otherwise considered part of the universe for the variable of interest.

4. income: The income category represents annual family income, rather than just an individual person's income. It is divided into five ranges: below $25K, $25K-49,999, $50K-74,999, $75K-99,999, and $100K or more. Statistics by income group are only available in this file for Supplements beginning in 2010; prior to 2010, family income range is available in public use datasets, but is not directly comparable to newer datasets due to the 2010 introduction of the practice of allocating "don't know," "refused," and other responses that result in missing data. Prior to 2010, family income is unkown for approximately 20 percent of persons, while in 2010 the Census Bureau began imputing likely income ranges to replace missing data.

5. education: Educational attainment is divided into "No Diploma," "High School Grad,

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Adoption & Child Custody Advocacy - Performance Measures Custody Investigations - 2010’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/39da3b96-b5ed-47dd-aa4b-ba17d08ebce9 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The department is court ordered to complete a home investigation to determine which home is most suitable for permanency or increased visitation

--- Original source retains full ownership of the source dataset ---

This dashboard includes those Foster and Adoptive homes that were screened and approved for child care through DFPS. Most of the FADs in the state are screened and approved for adoption through a private licensed child placing agency. Adoptive home – A home which has been screened and approved for adoption through DFPS. The Adoptive Home counts do not include homes open only for receipt of adoption subsidy. Foster-adoptive home – A home that has been approved to provide 24-hour residential care for a child, in accordance with Chapter 42 of the Human Resources Code and related regulations and that has been screened and approved have been approved for adoption through DFPS. Foster home – A home that has been approved to provide 24-hour residential care for a child, in accordance with Chapter 42 of the Human Resources Code and related regulation. Children in DFPS custody are those for whom a court has appointed DFPS legal responsibility through temporary or permanent managing conservatorship or other court ordered legal basis.

This chart counts the number of unique children in DFPS custody who lived in an adoptive placement at some point during the fiscal year and the total number of adoptive placements during the year. Children can have more than one adoptive placement. This chart includes children in DFPS custody for whom a court has appointed DFPS legal responsibility through Permanent Managing Conservatorship. An adoptive placement occurs when the child's caseworker, the family's case manager, and the adoptive family sign paperwork officially placing the child in the home for adoption. Before the paperwork can be signed, a child must be free for adoption (meaning a court has terminated parental rights), have a permanency goal of adoption and the family must have been approved for adoption through a licensed child placing agency. Visit dfps.state.tx.us for information on adoption and all DFPS programs.

As recommended by the Health and Human Services Commission (HHSC) to ensure consistency across all HHSC agencies, in 2012 DFPS adopted the HHSC methodology on how to categorize race and ethnicity. As a result, data broken down by race and ethnicity in 2012 and after is not directly comparable to race and ethnicity data in 2011 and before.

The population totals may not match previously printed DFPS Data Books. Past population estimates are adjusted based on the U.S. Census data as it becomes available. This is important to keep the data in line with current best practices, but may cause some past counts, such as Abuse/Neglect Victims per 1,000 Texas Children, to be recalculated.

Population Data Source - Population Estimates and Projections Program, Texas State Data Center, Office of the State Demographer and the Institute for Demographic and Socioeconomic Research, The University of Texas at San Antonio.

Current population estimates and projections data as of December 2020.

Visit dfps.texas.gov for information on all DFPS programs.

IntroductionAn important impediment to the large-scale adoption of evidence-based school nutrition interventions is the lack of evidence on effective strategies to implement them. This paper describes the protocol for a “Collaborative Network Trial” to support the simultaneous testing of different strategies undertaken by New South Wales Local Health Districts to facilitate the adoption of an effective school-based healthy lunchbox program (‘SWAP IT’). The primary objective of this study is to assess the effectiveness of different implementation strategies to increase school adoption of the SWAP across New South Wales Local Health Districts.MethodsWithin a Master Protocol framework, a collaborative network trial will be undertaken. Independent randomized controlled trials to test implementation strategies to increase school adoption of SWAP IT within primary schools in 10 different New South Wales Local Health Districts will occur. Schools will be randomly allocated to either the intervention or control condition. Schools allocated to the intervention group will receive a combination of implementation strategies. Across the 10 participating Local Health Districts, six broad strategies were developed and combinations of these strategies will be executed over a 6 month period. In six districts an active comparison group (containing one or more implementation strategies) was selected. The primary outcome of the trial will be adoption of SWAP IT, assessed via electronic registration records captured automatically following online school registration to the program. The primary outcome will be assessed using logistic regression analyses for each trial. Individual participant data component network meta-analysis, under a Bayesian framework, will be used to explore strategy-covariate interactions; to model additive main effects (separate effects for each component of an implementation strategy); two way interactions (synergistic/antagonistic effects of components), and full interactions.DiscussionThe study will provide rigorous evidence of the effects of a variety of implementation strategies, employed in different contexts, on the adoption of a school-based healthy lunchbox program at scale. Importantly, it will also provide evidence as to whether health service-centered, collaborative research models can rapidly generate new knowledge and yield health service improvements.Clinical trial registrationThis trial is registered prospectively with the Australian New Zealand Clinical Trials Registry (ACTRN12623000558628).

The purpose of this study was to establish a survey instrument to measure Chinese parents’ level of actions in their children’s private music classes. I adopted Fung’s framework of change and human actions as the theoretical support for a model of parents’ level of actions. Parents of 5- to 12-year-old children (N = 894) from 20 different provinces in China were surveyed on their level of involvement (i.e., proactivity, passivity, and avoidance) in their children’s private music education. Seven factors were extracted from the exploratory factor analysis, which were then consolidated into a 3-factor solution. Confirmatory factor analysis indicated an adequate model fit for the data collected from the Parents’ Level of Action in Private Music Learning Scale. The results from correlation analyses revealed that (1) children’s age had a direct but weak correlation with parents’ proactivity and (2) parents’ proactivity was positively associated with children’s intention to take music lessons. The results of the repeated-measures ANOVA indicated that most Chinese parents in this study were proactively involved in their children’s private music lessons. The findings from this study are consistent with the literature. Implications and recommendations are discussed, and suggestions for future research are included.