Evidence map›Paper›PMID 39582104›Full record

ArticleHealth expectations : an international journal of public participation in health care and health policy2024

Agenda Setting in Outpatient Consultation of Older Adults With Long-COVID.

Hao Zhao, Shuai Zhang, Wen Ma

Abstract read
In one paragraph

Article in Health expectations : an international journal of public participation in health care and health policy, 2024. 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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Hao ZhaoSchool of Foreign Languages and Literature, Shandong University, Jinan, China.ORCID 0009-0008-7654-0632
Shuai ZhangSchool of Foreign Languages, University of Jinan, Jinan, China.ORCID 0000-0001-9478-0412
Wen MaSchool of Foreign Languages and Literature, Shandong University, Jinan, China.ORCID 0000-0001-9931-9561

Funding

This study was supported by the Project of the National Social Science Fund of China 21BYY020, the Key Project of the National Social Science Fund of China 22AZD155, Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's, Republic of China 22YJC740100, and the Project of China Disabled Persons' Federation 2022CDPFHS-18.
6 · The paper itself

Abstract

introductionAgenda setting in doctor-patient interaction refers to the process when various agendas are presented and responded to. This study revealed how Long-COVID (LC) agenda items are managed by older adults and doctors during outpatient consultations.

methodsBased on audio recordings, we adopted Conversation Analysis (CA) to unveil under what circumstances the patient or the doctor set the agenda and how they collaboratively work to develop an LC topic. Data was transcribed in accordance with the transcription conventions developed by Jefferson.

resultsAgendas were divided into three categories, namely primary, additional, and unmet agenda items. LC agendas were identified with specific characteristics based on qualitative analysis and older adults tended to seek more medical assistance, particularly concerning their chronic diseases. We observed that patients initiate agendas more often than doctors and it could happen at any stage of the visit, both parties can resist expanding the agenda, mostly in an indirect way, and agendas that contain more information with simpler turn-constructions are more likely to be well-addressed.

conclusionsBetter quality of consultation was found when doctors sensitively detect the emotional change and the potential issues of the patients when they prevaricate to avoid the relevance of the previous infection. The findings also suggested that understanding the dynamics of agenda setting in consultations could lead to improvements in medical visit outcomes. PATIENT AND PUBLIC CONTRIBUTION: Patients and doctors in the study were colleagues in a university hospital. They were involved in various stages of this study to ensure it addresses real-world concerns and improves healthcare outcomes. Throughout data collection, patients contributed by allowing their consultations to be recorded and providing feedback on their experiences. Findings were discussed with a patient advisory group to ensure the interpretations aligned with patient perspectives. Doctors were also actively engaged in disseminating the results through later consultations, ensuring broad accessibility and practical application of the research outcomes.

Indexed as

COVID-19Physician-Patient RelationsAgedAged, 80 and overCommunicationFemaleHumansMaleMiddle AgedOutpatientsQualitative ResearchReferral and ConsultationSARS-CoV-2agenda settingconversation analysisdoctor–patient interactionlong‐COVIDolder adults

Identifiers

PMID39582104
PMCPMC11586236

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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.