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Mendeley readers
Attention Score in Context
| Title |
Applying psychological theories to evidence-based clinical practice: Identifying factors predictive of managing upper respiratory tract infections without antibiotics
|
|---|---|
| Published in |
Implementation Science, August 2007
|
| DOI | 10.1186/1748-5908-2-26 |
| Pubmed ID | |
| Authors |
Martin P Eccles, Jeremy M Grimshaw, Marie Johnston, Nick Steen, Nigel B Pitts, Ruth Thomas, Elizabeth Glidewell, Graeme Maclennan, Debbie Bonetti, Anne Walker |
| Abstract |
Psychological models can be used to understand and predict behaviour in a wide range of settings. However, they have not been consistently applied to health professional behaviours, and the contribution of differing theories is not clear. The aim of this study was to explore the usefulness of a range of psychological theories to predict health professional behaviour relating to management of upper respiratory tract infections (URTIs) without antibiotics. |
Mendeley readers
The data shown below were compiled from readership statistics for 201 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 8 | 4% |
| United States | 3 | 1% |
| Australia | 1 | <1% |
| Canada | 1 | <1% |
| Colombia | 1 | <1% |
| Unknown | 187 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 36 | 18% |
| Student > Ph. D. Student | 31 | 15% |
| Student > Master | 30 | 15% |
| Student > Bachelor | 16 | 8% |
| Professor > Associate Professor | 15 | 7% |
| Other | 46 | 23% |
| Unknown | 27 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 57 | 28% |
| Psychology | 34 | 17% |
| Social Sciences | 15 | 7% |
| Nursing and Health Professions | 15 | 7% |
| Agricultural and Biological Sciences | 9 | 4% |
| Other | 32 | 16% |
| Unknown | 39 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 10 November 2021.
All research outputs
#7,452,489
of 22,783,848 outputs
Outputs from Implementation Science
#1,246
of 1,721 outputs
Outputs of similar age
#24,417
of 67,163 outputs
Outputs of similar age from Implementation Science
#4
of 6 outputs
Altmetric has tracked 22,783,848 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 1,721 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. 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 67,163 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.