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A comparative analysis of centralized waiting lists for patients without a primary care provider implemented in six Canadian provinces: study protocol

Overview of attention for article published in BMC Health Services Research, January 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

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2 news outlets
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16 X users

Citations

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17 Dimensions

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94 Mendeley
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Title
A comparative analysis of centralized waiting lists for patients without a primary care provider implemented in six Canadian provinces: study protocol
Published in
BMC Health Services Research, January 2017
DOI 10.1186/s12913-017-2007-8
Pubmed ID
Authors

Mylaine Breton, Michael Green, Sara Kreindler, Jason Sutherland, Jalila Jbilou, Sabrina T. Wong, Jay Shaw, Valorie A. Crooks, Damien Contandriopoulos, Mélanie Ann Smithman, Astrid Brousselle

Abstract

Having a regular primary care provider (i.e., family physician or nurse practitioner) is widely considered to be a prerequisite for obtaining healthcare that is timely, accessible, continuous, comprehensive, and well-coordinated with other parts of the healthcare system. Yet, 4.6 million Canadians, approximately 15% of Canada's population, are unattached; that is, they do not have a regular primary care provider. To address the critical need for attachment, especially for more vulnerable patients, six Canadian provinces have implemented centralized waiting lists for unattached patients. These waiting lists centralize unattached patients' requests for a primary care provider in a given territory and match patients with providers. From the little information we have on each province's centralized waiting list, we know the way they work varies significantly from province to province. The main objective of this study is to compare the different models of centralized waiting lists for unattached patients implemented in six provinces of Canada to each other and to available scientific knowledge to make recommendations on ways to improve their design in an effort to increase attachment of patients to a primary care provider. A logic analysis approach developed in three steps will be used. Step 1: build logic models that describe each province's centralized waiting list through interviews with key stakeholders in each province; step 2: develop a conceptual framework, separate from the provincially informed logic models, that identifies key characteristics of centralized waiting lists for unattached patients and factors influencing their implementation through a literature review and interviews with experts; step 3: compare the logic models to the conceptual framework to make recommendations to improve centralized waiting lists in different provinces during a pan Canadian face-to-face exchange with decision-makers, clinicians and researchers. This study is based on an inter-provincial learning exchange approach where we propose to compare centralized waiting lists and analyze variations in strategies used to increase attachment to a regular primary care provider. Fostering inter-provincial healthcare systems connectivity to improve centralized waiting lists' practices across Canada can lever attachment to a regular provider for timely access to continuous, comprehensive and coordinated healthcare for all Canadians and particular for those who are vulnerable.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 1%
Unknown 93 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 14%
Student > Ph. D. Student 10 11%
Student > Bachelor 10 11%
Other 6 6%
Researcher 6 6%
Other 23 24%
Unknown 26 28%
Readers by discipline Count As %
Nursing and Health Professions 19 20%
Medicine and Dentistry 17 18%
Social Sciences 7 7%
Psychology 4 4%
Business, Management and Accounting 3 3%
Other 10 11%
Unknown 34 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 02 March 2020.
All research outputs
#1,232,967
of 25,262,379 outputs
Outputs from BMC Health Services Research
#352
of 8,576 outputs
Outputs of similar age
#26,002
of 430,057 outputs
Outputs of similar age from BMC Health Services Research
#5
of 138 outputs
Altmetric has tracked 25,262,379 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,576 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done particularly well, scoring higher than 95% 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 430,057 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.