Title |
Development of a Management Algorithm for Post-operative Pain (MAPP) after total knee and total hip replacement: study rationale and design
|
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Published in |
Implementation Science, August 2014
|
DOI | 10.1186/s13012-014-0110-3 |
Pubmed ID | |
Authors |
Mari Botti, Bridie Kent, Tracey Bucknall, Maxine Duke, Megan-Jane Johnstone, Julie Considine, Bernice Redley, Susan Hunter, Richard de Steiger, Marlene Holcombe, Emma Cohen |
Abstract |
Evidence from clinical practice and the extant literature suggests that post-operative pain assessment and treatment is often suboptimal. Poor pain management is likely to persist until pain management practices become consistent with guidelines developed from the best available scientific evidence. This work will address the priority in healthcare of improving the quality of pain management by standardising evidence-based care processes through the incorporation of an algorithm derived from best evidence into clinical practice. In this paper, the methodology for the creation and implementation of such an algorithm that will focus, in the first instance, on patients who have undergone total hip or knee replacement is described. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Australia | 1 | 17% |
United States | 1 | 17% |
Canada | 1 | 17% |
Unknown | 3 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 2 | 33% |
Scientists | 2 | 33% |
Members of the public | 1 | 17% |
Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | <1% |
United States | 1 | <1% |
Brazil | 1 | <1% |
Egypt | 1 | <1% |
Unknown | 103 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 21 | 20% |
Student > Postgraduate | 11 | 10% |
Student > Ph. D. Student | 11 | 10% |
Student > Bachelor | 10 | 9% |
Student > Doctoral Student | 9 | 8% |
Other | 25 | 23% |
Unknown | 20 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 47 | 44% |
Nursing and Health Professions | 18 | 17% |
Business, Management and Accounting | 2 | 2% |
Engineering | 2 | 2% |
Social Sciences | 2 | 2% |
Other | 14 | 13% |
Unknown | 22 | 21% |