Evidence map›Paper›PMID 39049410›Full record

ArticleStudies in health technology and informatics2024

Towards Building an E-Librarian System: Exploring Recommendation System Preferences Among Ovarian Cancer Patients and Caregivers.

Vivian Hui, Youjia Wang, Hannah Kunsuk, Heidi Donovan, Peter Brusilovsky, Daqing He, Young Ji Lee

Abstract read
In one paragraph

Article in Studies in health technology and informatics, 2024. 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

7 authors.

Vivian HuiCentre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR.
Youjia WangHealth and Community Systems, School of Nursing, University of Pittsburgh, PA, USA.
Hannah KunsukDepartment of Biological Sciences, University of Pittsburgh, USA.
Heidi DonovanHealth and Community Systems, School of Nursing, University of Pittsburgh, PA, USA.
Peter BrusilovskySchool of Computing and Information Science, University of Pittsburgh, PA, USA.
Daqing HeSchool of Computing and Information Science, University of Pittsburgh, PA, USA.
Young Ji LeeHealth and Community Systems, School of Nursing, University of Pittsburgh, PA, USA.

Funding

Development and Implementation of a Health e-Librarian with Personalized Recommender (HELPeR)R01LM013038 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HE, DAQING, LEE, YOUNG JI · 2019 to 2022
$1.3M
NLM NIH HHS R01 LM013038
6 · The paper itself

Abstract

Ovarian cancer (OvCa) patients encounter complex treatment decisions, and often have difficulties in searching and integrating online health information to guide their treatment decision-making. The objective of this study was to explore the preference of online health information among OvCa patients and caregivers, by exploring their preferred content, format, and function features for the design of a personalized recommender system. This study used qualitative research methods to collect data through in-depth interviews with 18 OvCa patients and 2 caregivers. A total of (N=20) face-to-face interviews were conducted, and subsequently analyzed by audio recordings, verbatim transcription, and theory-driven approach with thematic analysis. A total of 5 themes were identified for content-related design, 4 themes identified for system function and one theme identified for frequency format. The results of this study inform the preference and therefore OvCa specific features can be tailor-made in a recommendation system.

Indexed as

CaregiversOvarian NeoplasmsPatient PreferenceAdultAgedFemaleHumansInformation Storage and RetrievalInterviews as TopicMiddle AgedCOVID-19gynecological canceronline health information seeking (OHIS)Ovarian cancer

Identifiers

PMID39049410
PMCPMC12087006

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.