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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, July 2013
|
| DOI | 10.1186/1472-6947-13-71 |
| Pubmed ID | |
| Authors |
Andrea C Fernandes, Danielle Cloete, Matthew TM Broadbent, Richard D Hayes, Chin-Kuo Chang, Richard G Jackson, Angus Roberts, Jason Tsang, Murat Soncul, Jennifer Liebscher, Robert Stewart, Felicity Callard |
| Abstract |
Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 22% |
| United Kingdom | 1 | 11% |
| United States | 1 | 11% |
| Argentina | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 6 | 67% |
| Members of the public | 3 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 186 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | <1% |
| Unknown | 185 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 30 | 16% |
| Researcher | 27 | 15% |
| Student > Ph. D. Student | 23 | 12% |
| Student > Bachelor | 11 | 6% |
| Student > Postgraduate | 10 | 5% |
| Other | 39 | 21% |
| Unknown | 46 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 30 | 16% |
| Computer Science | 27 | 15% |
| Psychology | 19 | 10% |
| Social Sciences | 16 | 9% |
| Nursing and Health Professions | 6 | 3% |
| Other | 26 | 14% |
| Unknown | 62 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 21 February 2014.
All research outputs
#7,029,155
of 25,464,544 outputs
Outputs from BMC Medical Informatics and Decision Making
#609
of 2,145 outputs
Outputs of similar age
#55,634
of 206,773 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#12
of 34 outputs
Altmetric has tracked 25,464,544 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 2,145 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 71% of its peers.
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 206,773 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.