Evidence map›Paper›PMID 30893443›Full record

ArticleResearch in gerontological nursing2019

Factors Related to Medication Self-Management in African American Older Women.

Julie L Ellis, Christine R Kovach, Michael Fendrich, Oluwatoyin Olukotun, Vanessa K Baldwin, Weiming Ke, Barbara Nichols

Open access · bronzeAbstract read
In one paragraph

Article in Research in gerontological nursing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.9field-weighted citation impact, top 16% of its field
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, 16 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
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

7 authors at 4 institutions in 1 country.

Julie L Ellis
Christine R Kovach
Michael Fendrich
Oluwatoyin Olukotun
Vanessa K Baldwin
Weiming Ke
Barbara Nichols
University of Wisconsin–Milwaukee · USCGFNS International, Inc.University of Connecticut · USUniversity of Hartford · US

Funding

Self-Management Science Center at UWMP20NR015339 · NINR · UNIVERSITY OF WISCONSIN MILWAUKEE · PI FENDRICH, MICHAEL · 2014 to 2018
$1.4M
NINR NIH HHS P20 NR015339
6 · The paper itself

Abstract

Individuals with multiple chronic diseases are often prescribed medications for each condition and thus must manage a drug regimen. Medication self-management is challenging for most individuals with chronic diseases, but it can be especially difficult for African American older women. This study investigated how medical mistrust, caregiver role strain, and other relevant variables may be associated with medication self-management behaviors (MSMB) among African American older women, and whether goal congruence and self-efficacy mediated the relationship between the predictor variables and MSMB. A sample of 116 African American older (age >50 years) women from central Milwaukee participated in this correlational, cross-sectional study. Although goal congruence and self-efficacy were not found to act as mediators, the main finding was that goal congruence, self-efficacy, and age predicted 30% of the variance in MSMB. The results suggest that it is essential to strengthen individual self-efficacy, determine the goals that individuals have for their medication regimen, and develop support mechanisms to help patients attain these goals to better manage chronic disease. [Res Gerontol Nurs. 2019; 12(2):71-79.].

Indexed as

Black or African AmericanMedication AdherenceSelf-ManagementAgedAged, 80 and overCross-Sectional StudiesFemaleHumansMiddle AgedWisconsin

Identifiers

PMID30893443
PMCPMC6918485
OpenAlexW2923191688

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.