Evidence map›Paper›PMID 41876107›Full record

ArticleAnnals of family medicine2026

When High Scores Hide Realities: Enhancing Patient Survey Data Through Joint Display.

Meaghan Brierley, Fatemeh Vakilian, Mahnoush Rostami

Abstract read
In one paragraph

Article in Annals of family medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Meaghan BrierleyHealth Systems Knowledge and Evaluation, Acute Care Alberta, Edmonton, Alberta, Canada meaghan.brierley@acutecarealberta.ca.
Fatemeh VakilianHealth Systems Knowledge and Evaluation, Acute Care Alberta, Edmonton, Alberta, Canada.
Mahnoush RostamiHealth Systems Knowledge and Evaluation, Acute Care Alberta, Edmonton, Alberta, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePatient surveys often include closed- and open-ended responses that are usually reported separately, and links between them rarely explored, limiting interpretive depth. We outline a strategy that integrates quantitative and qualitative survey data in a joint display which enables combined analysis and findings to inform and improve programs for patients.

methodsData were drawn from a patient experience survey implemented in Alberta's long COVID Inter-professional Outpatient Program (IPOP). A joint display of Likert-scale satisfaction ratings and themed open-text feedback was developed to examine connections between the structured responses and qualitative insights.

resultsWe integrated quantitative survey satisfaction ratings with themes from the qualitative analysis of open-text comments in a joint display. Examining these data sets together added an analytical layer and uncovered nuanced experiences not evident in Likert-scale responses alone. By aligning satisfaction scores with themes, the joint display surfaced contradictory and paradoxical findings that would otherwise be hidden.

conclusionsAligning Likert-scale responses with thematic analysis of open-text reveals subtleties that may be obscured by scores alone. Understanding the narratives behind ratings is essential to evaluate health care programming, particularly when surveys are the primary mechanism for incorporating patient voices into service planning and delivery.

Indexed as

COVID-19Patient SatisfactionAlbertaHumansQualitative ResearchSARS-CoV-2Surveys and Questionnaireshealth services researchjoint displaymixed methodspatient satisfactionprogram evaluationquality improvementsurvey methods

Identifiers

PMID41876107
PMCPMC13008812

What OpenQuestion holds

Textmetadata
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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.