Evidence map›Paper›PMID 42773358›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2026

Beyond one-size-fits-all: mapping information-seeking and decision-making pathways in cancer care.

Neta Shanwetter Levit, Mor Saban

Abstract read
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Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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3 · Its place in the literature

Who cites it

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Neta Shanwetter LevitSchool of Health Professions, Gray Faculty of Medical and Health Sciences, Tel Aviv University, P.O. Box 39040, 69978, Tel Aviv, Israel. netas3@mail.tau.ac.il.
Mor SabanSchool of Health Professions, Gray Faculty of Medical and Health Sciences, Tel Aviv University, P.O. Box 39040, 69978, Tel Aviv, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo identify distinct archetypes among adult cancer patients based on information-seeking patterns from symptom onset to treatment initiation, including the use of digital and emerging technologies such as generative AI, and to characterize the decision-making dilemmas associated with these pathways.

methodsWe conducted a cross-sectional study in Israel, between February 2025 and December 2025, among 205 adult cancer patients to examine patterns of information seeking and decision-making during the period from symptom onset to treatment initiation. Participants completed a structured questionnaire assessing reliance on multiple information sources-including medical professionals, family members, digital resources, and generative AI-based tools-before and after diagnosis, along with sociodemographic and clinical characteristics. Patient archetypes were identified using cluster analysis, and decision-making dilemmas were explored using two methods (investigator-led constant comparative analysis and LLM) for open-ended responses.

resultsFour distinct information-seeking archetypes were identified: Digital Natives (27.6%), Family-Centered (38.8%), Balanced Traditional (25.0%), and Medical Professional-Focused (8.6%). Archetypes differed significantly by age, education, and the strongest differentiating factor (p = 0.005)-religiosity. Across archetypes, information seeking intensified after diagnosis, relying mostly on family members, internet sources, and additional medical professionals, whereas reliance on generative AI-based tools remained consistently low. Among respondents to the open-ended question, 86% reported significant decision-making dilemmas, most commonly related to treatment selection and choice of healthcare provider or facility.

conclusionThe marked heterogeneity observed in patients' information-seeking and decision-making pathways highlights the inadequacy of one-size-fits-all approaches in early cancer care. Implementing tailored, culturally responsive decision support may better align care with patients' needs during this critical phase.

Indexed as

Decision MakingInformation Seeking BehaviorNeoplasmsAdultAgedCross-Sectional StudiesDigital HealthFemaleGenerative Artificial IntelligenceHumansIsraelMaleMiddle AgedSurveys and QuestionnairesCancer-care navigationDecision supportOncologyPatient-centered careShared decision-making

Identifiers

PMID42773358
PMCPMC13597558

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.