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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Health care quality measures for children and adolescents in Foster Care: feasibility testing in electronic records
|
|---|---|
| Published in |
BMC Pediatrics, February 2018
|
| DOI | 10.1186/s12887-018-1064-4 |
| Pubmed ID | |
| Authors |
Katherine J. Deans, Peter C. Minneci, Kristine M. Nacion, Karen Leonhart, Jennifer N. Cooper, Sarah Hudson Scholle, Kelly J. Kelleher |
| Abstract |
Preventive quality measures for the foster care population are largely untested. The objective of the study is to identify healthcare quality measures for young children and adolescents in foster care and to test whether the data required to calculate these measures can be feasibly extracted and interpreted within an electronic health records or within the Statewide Automated Child Welfare Information System. The AAP Recommendations for Preventive Pediatric Health Care served as the guideline for determining quality measures. Quality measures related to well child visits, developmental screenings, immunizations, trauma-related care, BMI measurements, sexually transmitted infections and depression were defined. Retrospective chart reviews were performed on a cohort of children in foster care from a single large pediatric institution and related county. Data available in the Ohio Statewide Automated Child Welfare Information System was compared to the same population studied in the electronic health record review. Quality measures were calculated as observed (received) to expected (recommended) ratios (O/E ratios) to describe the actual quantity of recommended health care that was received by individual children. Electronic health records and the Statewide Automated Child Welfare Information System data frequently lacked important information on foster care youth essential for calculating the measures. Although electronic health records were rich in encounter specific clinical data, they often lacked custodial information such as the dates of entry into and exit from foster care. In contrast, Statewide Automated Child Welfare Information System included robust data on custodial arrangements, but lacked detailed medical information. Despite these limitations, several quality measures were devised that attempted to accommodate these limitations. In this feasibility testing, neither the electronic health records at a single institution nor the county level Statewide Automated Child Welfare Information System was able to independently serve as a reliable source of data for health care quality measures for foster care youth. However, the ability to leverage both sources by matching them at an individual level may provide the complement of data necessary to assess the quality of healthcare. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 81 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 11% |
| Student > Ph. D. Student | 8 | 10% |
| Researcher | 7 | 9% |
| Student > Bachelor | 5 | 6% |
| Unspecified | 5 | 6% |
| Other | 15 | 19% |
| Unknown | 32 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 14% |
| Nursing and Health Professions | 9 | 11% |
| Unspecified | 5 | 6% |
| Social Sciences | 4 | 5% |
| Materials Science | 3 | 4% |
| Other | 16 | 20% |
| Unknown | 33 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2018.
All research outputs
#15,498,204
of 23,031,582 outputs
Outputs from BMC Pediatrics
#2,061
of 3,039 outputs
Outputs of similar age
#211,318
of 330,910 outputs
Outputs of similar age from BMC Pediatrics
#72
of 97 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,039 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,910 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 97 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.