Evidence map›Paper›PMID 36044917›Full record

ArticleApplied clinical informatics2022

Coincidence Analysis: A Novel Approach to Modeling Nurses' Workplace Experience.

Dana M Womack, Edward J Miech, Nicholas J Fox, Linus C Silvey, Anna M Somerville, Deborah H Eldredge, Linsey M Steege

Open access · bronzeAbstract read
In one paragraph

Article in Applied clinical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 4 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. 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

7 authors at 3 institutions in 1 country.

Dana M WomackDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Edward J MiechRegenstrief Institute, Indianapolis, Indiana, United States.
Nicholas J FoxDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Linus C SilveyDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Anna M SomervilleDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Deborah H EldredgeDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Linsey M SteegeSchool of Nursing, University of Wisconsin-Madison, Madison, Wisconsin, United States.
Oregon Health & Science University · USRegenstrief Institute · USUniversity of Wisconsin–Madison · US

Funding

NW Center of Excellence & K12 in Patient Centered Learning Health Systems ScienceK12HS026370 · AHRQ · OREGON HEALTH & SCIENCE UNIVERSITY · PI EDEN, KAREN BEEKMAN, GUISE, JEANNE-MARIE · 2018 to 2022
$4.0M
AHRQ HHS K12 HS026370
6 · The paper itself

Abstract

objectivesThe purpose of this study is to identify combinations of workplace conditions that uniquely differentiate high, medium, and low registered nurse (RN) ratings of appropriateness of patient assignment during daytime intensive care unit (ICU) work shifts.

methodsA collective case study design and coincidence analysis were employed to identify combinations of workplace conditions that link directly to high, medium, and low RN perception of appropriateness of patient assignment at a mid-shift time point. RN members of the study team hypothesized a set of 55 workplace conditions as potential difference makers through the application of theoretical and empirical knowledge. Conditions were derived from data exported from electronic systems commonly used in nursing care.

resultsAnalysis of 64 cases (25 high, 24 medium, and 15 low) produced three models, one for each level of the outcome. Each model contained multiple pathways to the same outcome. The model for "high" appropriateness was the simplest model with two paths to the outcome and a shared condition across pathways. The first path comprised of the absence of overtime and a before-noon patient discharge or transfer, and the second path comprised of the absence of overtime and RN assignment to a single ICU patient.

conclusionSpecific combinations of workplace conditions uniquely distinguish RN perception of appropriateness of patient assignment at a mid-shift time point, and these difference-making conditions provide a foundation for enhanced observability of nurses' work experience during hospital work shifts. This study illuminates the complexity of assessing nursing work system status by revealing that multiple paths, comprised of multiple conditions, can lead to the same outcome. Operational decision support tools may best reflect the complex adaptive nature of the work systems they intend to support by utilizing methods that accommodate both causal complexity and equifinality.

Indexed as

NursesWorkplaceHumans

Identifiers

PMID36044917
PMCPMC9433166
OpenAlexW4294127803

What OpenQuestion holds

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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.