Evidence map›Paper›PMID 35682389›Full record

ArticleInternational journal of environmental research and public health2022

Bedside Teaching in Rural Family Medicine Education in Japan.

Ryuichi Ohta, Chiaki Sano

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

2 authors.

Ryuichi OhtaCommunity Care, Unnan City Hospital, 96-1 Iida, Daito-cho, Unnan 699-1221, Japan.ORCID 0000-0003-2593-091X
Chiaki SanoDepartment of Community Medicine Management, Faculty of Medicine, Shimane University, 89-1 Enya cho, Izumo 693-8501, Japan.ORCID 0000-0002-9796-5161

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bedside teaching is essential in family medicine education so that residents may learn about various clinical conditions and develop professional skills. In particular, bedside teaching is useful in a rural context because rural family medicine deals with a broad scope of biopsychosocial problems among older patients. Accordingly, based on an inductive thematic analysis, we propose a framework for bedside teaching in rural family medicine education, which consists of four themes: accommodation of different learners, near-peer learning, the change in engagement of medical teachers in bedside teaching, and driving interpersonal collaboration. Bedside teaching can promote interactions between different medical learners. Near-peer learning in bedside teaching compensates for the limited availability of educators and improves learners' motivation for self-directed learning. Through bedside teaching, medical learners can observe each other and provide constructive feedback, thereby improving their relationships and learning. For effective bedside teaching, medical educators should facilitate learners and collaborate with other medical professionals. Additionally, bedside teaching should accommodate a variety of learners, facilitate near-peer and self-directed learning, educators' involvement based on cognitive apprenticeship, along with interprofessional collaboration with nurses. Interprofessional collaboration between rural family medicine teachers, learners, and nurses may improve the quality of patient care due to the increased understanding between patients and other medical staff in hospitals.

Indexed as

Family PracticeLearningHumansJapanTeachingbedside teachingcommunity hospitalsJapanmedical residentmedical teachernursesrural family education

Identifiers

PMID35682389
PMCPMC9180610

What OpenQuestion holds

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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.