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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Evaluation of data quality of interRAI assessments in home and community care
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, October 2017
|
| DOI | 10.1186/s12911-017-0547-9 |
| Pubmed ID | |
| Authors |
Sophie E. Hogeveen, Jonathan Chen, John P. Hirdes |
| Abstract |
The aim of this project is to describe the quality of assessment data regularly collected in home and community, with techniques adapted from an evaluation of the quality of long-term care data in Canada. Data collected using the Resident Assessment Instrument - Home Care (RAI-HC) in Ontario and British Columbia (BC) as well as the interRAI Community Health Assessment (CHA) in Ontario were analyzed using descriptive statistics, Pearson's r correlation, and Cronbach's alpha in order to assess trends in population characteristics, convergent validity, and scale reliability. Results indicate that RAI-HC data from Ontario and BC behave in a consistent manner, with stable trends in internal consistency providing evidence of good reliability (alpha values range from 0.72-0.94, depending on the scale and province). The associations between various scales, such as those reflecting functional status and cognition, were found to be as expected and stable over time within each setting (r values range from 0.42-0.45 in Ontario and 0.41-0.43 in BC). These trends in convergent validity demonstrate that constructs in the data behave as they should, providing evidence of good data quality. In most cases, CHA data quality matches that of RAI-HC data quality and shows evidence of good validity and reliability. The findings are comparable to the findings observed in the evaluation of data from the long-term care sector. Despite an increasingly complex client population in the home and community care sectors, the results from this work indicate that data collected using the RAI-HC and the CHA are of an overall quality that may be trusted when used to inform decision-making at the organizational- or policy-level. High quality data and information are vital when used to inform steps taken to improve quality of care and enhance quality of life. This work also provides evidence that a method used to evaluate the quality of data obtained in the long-term care setting may be used to evaluate the quality of data obtained through community-based measures. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 85 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 85 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 15 | 18% |
| Student > Ph. D. Student | 12 | 14% |
| Researcher | 10 | 12% |
| Student > Bachelor | 6 | 7% |
| Librarian | 4 | 5% |
| Other | 10 | 12% |
| Unknown | 28 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 17 | 20% |
| Social Sciences | 10 | 12% |
| Medicine and Dentistry | 10 | 12% |
| Business, Management and Accounting | 5 | 6% |
| Computer Science | 3 | 4% |
| Other | 5 | 6% |
| Unknown | 35 | 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 08 October 2020.
All research outputs
#16,769,630
of 24,666,614 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,391
of 2,103 outputs
Outputs of similar age
#212,468
of 334,205 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 26 outputs
Altmetric has tracked 24,666,614 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,103 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 25th percentile – i.e., 25% 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 334,205 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 26 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.