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Measuring management’s perspective of data quality in Pakistan’s Tuberculosis control programme: a test-based approach to identify data quality dimensions

Overview of attention for article published in BMC Research Notes, January 2018
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
1 tweeter
googleplus
1 Google+ user

Citations

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1 Dimensions

Readers on

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52 Mendeley
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Title
Measuring management’s perspective of data quality in Pakistan’s Tuberculosis control programme: a test-based approach to identify data quality dimensions
Published in
BMC Research Notes, January 2018
DOI 10.1186/s13104-018-3161-8
Pubmed ID
Authors

Syed Mustafa Ali, Naveed Anjum, Maged N. Kamel Boulos, Muhammad Ishaq, Javariya Aamir, Ghulam Rasool Haider

Abstract

Data quality is core theme of programme's performance assessment and many organizations do not have any data quality improvement strategy, wherein data quality dimensions and data quality assessment framework are important constituents. As there is limited published research about the data quality specifics that are relevant to the context of Pakistan's Tuberculosis control programme, this study aims at identifying the applicable data quality dimensions by using the 'fitness-for-purpose' perspective. Forty-two respondents pooled a total of 473 years of professional experience, out of which 223 years (47%) were in TB control related programmes. Based on the responses against 11 practical cases, adopted from the routine recording and reporting system of Pakistan's TB control programme (real identities of patient were masked), completeness, accuracy, consistency, vagueness, uniqueness and timeliness are the applicable data quality dimensions relevant to the programme's context, i.e. work settings and field of practice. Based on a 'fitness-for-purpose' approach to data quality, this study used a test-based approach to measure management's perspective and identified data quality dimensions pertinent to the programme and country specific requirements. Implementation of a data quality improvement strategy and achieving enhanced data quality would greatly help organizations in promoting data use for informed decision making.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 17%
Student > Bachelor 9 17%
Student > Ph. D. Student 7 13%
Researcher 7 13%
Professor 3 6%
Other 6 12%
Unknown 11 21%
Readers by discipline Count As %
Nursing and Health Professions 10 19%
Medicine and Dentistry 9 17%
Computer Science 6 12%
Business, Management and Accounting 3 6%
Engineering 3 6%
Other 9 17%
Unknown 12 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 January 2018.
All research outputs
#6,710,464
of 12,379,409 outputs
Outputs from BMC Research Notes
#997
of 2,768 outputs
Outputs of similar age
#144,129
of 337,420 outputs
Outputs of similar age from BMC Research Notes
#27
of 64 outputs
Altmetric has tracked 12,379,409 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,768 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 61% 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 337,420 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 55% of its contemporaries.
We're also able to compare this research output to 64 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 54% of its contemporaries.