Evidence map›Paper›PMID 39170802›Full record

ArticleJournal of mixed methods research2024

Enhancing Qualitative and Quantitative Data Linkages in Complex Mixed Methods Designs: Illustrations from a Multi-Phase Healthcare Delivery Study.

P Paul Chandanabhumma, Sriram Swaminathan, Lourdes M Cabrera, Hechuan Hou, Guangyu Yang, K Dennie Kim, Allison M Janda, Khalil Nassar, Preeti N Malani, Min Zhang and 5 more

Abstract read
In one paragraph

Article in Journal of mixed methods research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Observational
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

P Paul ChandanabhummaDepartment of Family Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-3079-5591
Sriram SwaminathanDepartment of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0009-0003-3281-3612
Lourdes M CabreraDepartment of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-0821-4340
Hechuan HouDepartment of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-0890-0746
Guangyu YangDepartment of Statistics, School of Public Health, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-4692-1361
K Dennie KimStrategy, Ethics, and Entrepreneurship, Darden School of Business, University of Virginia, Charlottesville, VA.ORCID 0000-0001-5718-6545
Allison M JandaDepartment of Anesthesiology, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0001-5080-5630
Khalil NassarMichigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0001-9607-8037
Preeti N MalaniDepartment of Internal Medicine, Division of Infectious Diseases, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0002-8263-8408
Min ZhangDepartment of Statistics, School of Public Health, University of Michigan, Ann Arbor, MI.ORCID 0000-0003-3331-3583
Russell J FunkDepartment of Strategic Management and Entrepreneurship, Carlson School of Management, University of Minnesota, Minneapolis, MN.ORCID 0000-0001-6670-4981
Keith D AaronsonDepartment of Internal Medicine, Division of Cardiovascular Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0003-1538-576X
Justine WuDepartment of Family Medicine, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0003-4646-8410
Francis D PaganiDepartment of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0003-4430-9969
Donald S LikoskyDepartment of Cardiac Surgery, Michigan Medicine, University of Michigan, Ann Arbor, MI.ORCID 0000-0003-0416-9359

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Understanding and Addressing Variation in Healthcare-Associated Infections After Durable Ventricular Assist Device TherapyR01HS026003 · AHRQ · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LIKOSKY, DONALD S · 2018 to 2022
$2.3M
PHEnylephrine versus NOrepinephrine in Major NONcardiac surgery (PHENOMeNON): Foundational Studies for a Pragmatic Randomized Clinical Trial.K23HL166685 · NHLBI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Allison Mary Janda · 2023 to 2026
$689k
AHRQ HHS R01 HS026003NCATS NIH HHS UL1 TR002240NHLBI NIH HHS K23 HL166685
6 · The paper itself

Abstract

While mixed methods research is increasingly used to examine determinants of unwarranted variability in healthcare delivery and outcomes, novel integrative approaches are required to meet the needs of mixed methods healthcare delivery research. This article describes novel refining strategies that enhance the linkage between qualitative and quantitative dimensions of a mixed methods healthcare delivery research study. Leveraging our study experiences, this paper demonstrates several refining strategies: (1) using mediated allocation concealment to facilitate qualitative sampling; (2) informing qualitative inquiry through quantitative analytics; and (3) training and immersing multidisciplinary researchers in qualitative data collection and analysis. Developing and implementing strategies in mixed methods healthcare delivery research could advance methodological rigor and strengthen multidisciplinary collaboration.

Indexed as

healthcare deliveryheart failuremixed methodsteam science

Identifiers

PMID39170802
PMCPMC11335019

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.