Evidence map›Paper›PMID 39687833›Full record

ReviewCureus2024

The Role of Scottish Languages and Dialects in Integrating International Medical Graduates Into the National Health Service: Navigating Linguistic Barriers.

Usama Al-Kamali, Goran Zangana, Matheel Al-Rawas

Abstract readReview
In one paragraph

Review in Cureus, 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

3 authors.

Usama Al-KamaliUnit of Acute and General Medicine, Royal Infirmary of Edinburgh, Edinburgh, GBR.
Goran ZanganaUnit of Acute and General Medicine, Royal Infirmary of Edinburgh, Edinburgh, GBR.
Matheel Al-RawasProsthodontics, School of Dental Sciences, Universiti Sains Malaysia, Kota Bharu, MYS.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The United Kingdom, particularly Scotland, is a key destination for international medical graduates (IMGs), who now make up a substantial part of the National Health Service (NHS) workforce. These IMGs encounter several challenges when integrating into the NHS, with language barriers being especially significant. Although many IMGs are educated in English, they frequently struggle with the intricacies of Scottish languages and dialects, which are vital for good patient care. This review examines Scotland's linguistic environment, focusing on the roles of Gaelic and Scots languages in cultural distinctiveness and patient communication. By means of a literature review and focus group interviews with IMGs, the authors ascertained commonly utilised Scottish colloquialisms and their connotations, highlighting their importance in clinical contexts. The findings indicate that comprehending such colloquialisms can greatly improve doctor-patient communication, decrease misunderstandings, and enhance health outcomes. The article advocates for the formulation of formal training programs to better equip IMGs for the linguistic challenges they will encounter, thus improving their assimilation into the NHS and enhancing patient care. While the Scottish Government's efforts to support international recruitment and workforce assimilation have been exemplary, there remains a pressing need for targeted language orientation to close the communication gap and warrant high-quality healthcare delivery.

Indexed as

clinical practicecross-cultural communicationinternational medical graduateslanguage barriers in healthcarenational health servicescottish dialectsscottish nhs integration

Identifiers

PMID39687833
PMCPMC11648978

What OpenQuestion holds

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LicenceCC BY
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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.