Evidence map›Paper›PMID 25848230›Full record

ArticlePatient preference and adherence2015

Development of the CoMac Adherence Descriptor™: a linguistically-based survey for segmenting patients on their worldviews.

Ulla M Connor, Robert S Mac Neill, Howard R Mzumara, Robert Sandy

Abstract read
In one paragraph

Article in Patient preference and adherence, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Ulla M ConnorInternational Center for Intercultural Communication (ICIC), Indiana University - Purdue University, Indianapolis, IN, USA.
Robert S Mac NeillInternational Center for Intercultural Communication (ICIC), Indiana University - Purdue University, Indianapolis, IN, USA.
Howard R MzumaraTesting Center - Division of Planning and Institutional Improvement, Indiana University - Purdue University, Indianapolis, IN, USA.
Robert SandyInternational Center for Intercultural Communication (ICIC), Indiana University - Purdue University, Indianapolis, IN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonadherence to prescribed medication and healthy behaviors is a pressing health care issue. Much research has been conducted in this area under a variety of labels, such as compliance, disease management and, most recently, adherence. However, the complex factors related to predicting and, more importantly, understanding and explaining adherence, have nevertheless remained elusive. However, through an in-depth linguistic analysis of patient talk, the International Center for Intercultural Communication (ICIC) at Indiana University has produced a psycholinguistic coding system that uses patients' own language to cluster them into distinct groups based on their worldviews. ICIC's studies have shown, for example, that patients reveal their fundamental perceptions about themselves and their environment in their life narratives; clustering of individual patients based on these different perceptions is possible via the use of differential language in survey questions, and differential language can be used to tailor messages for individual patients in a manner that these individuals prefer over generically worded communication. In grant-funded research, an interdisciplinary team of researchers at the ICIC reviewed the literature and identified three basic psychosocial tenets related to adherence: control orientation, based on locus of control research; agency, based on self-efficacy; and affect or attitude and emotion. These three constructs were selected because, in the published literature, they have been consistently found to be connected to patient adherence. Based on this research, a survey, the CoMac Descriptor™ was developed. This report shows that The Descriptor™ questions and responses are valid and reliable in segmenting patients across psychosocial constructs, which will have positive implications for health care providers and patients.

Indexed as

adherencecommunicationdiabetes

Identifiers

PMID25848230
PMCPMC4381900

What OpenQuestion holds

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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.