Evidence map›Paper›PMID 40746202›Full record

ArticleCancer medicine2025

Improving Ethnic Diversity in Cancer Trials Through Healthcare Interpreter Training.

Suzanne J Grant, Mayra Ouriques, Abhijit Pal, Sharon Lee, Sheetal Challam, Lindsey Jasicki

Abstract read
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Suzanne J GrantNICM Health Research Institute, Western Sydney University, Penrith South, DC New South Wales, Australia.ORCID https://orcid.org/0000-0002-9491-203X
Mayra OuriquesCancer Institute NSW, Sydney, NSW, Australia.
Abhijit PalLiverpool Hospital and Bankstown Hospital- South Western Sydney Local Health District, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0002-4600-6569
Sharon LeeUniversity of Sydney, Faculty of Medicine and Health, Sydney, Australia.ORCID https://orcid.org/0000-0001-8128-7776
Sheetal ChallamCancer Institute NSW, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0002-8227-0842
Lindsey JasickiCancer Institute NSW, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionPeople with cancer from culturally and linguistically diverse (CALD) backgrounds who are not proficient in English face many challenges in accessing clinical trials. Clinical trials offer opportunities to access cutting-edge therapies for cancer management, with opportunities for longer survival and/or better quality of life. Inequitable access to these clinical trials not only reduces the validity of research findings, but also exacerbates the known disparities in cancer outcomes for these populations. Australia is a migrant majority country, with certain areas having large proportions of people who do not speak English-research has shown that this group has a lower rate of trial participation than those who can speak English. There is no available specific training in cancer clinical trials or research terminology for healthcare interpreters (HCIs). Research has shown that inadequately trained interpreters are a recognized barrier to clinical trial access for patients who are not proficient in English. This two-phase quality improvement project, including a baseline knowledge survey and subsequent training modules, was undertaken to build workforce capacity for interpreters in cancer clinical trials.

methodsPhase 1: Subject matter experts and NSW Healthcare Interpreting Services managers codeveloped a survey to identify knowledge and skill gaps. HCIs across NSW (approx. 700) were invited to participate in a survey via an anonymous link (Qualtrics). Phase 2: Training was developed comprising five sections (basic concepts of clinical trials, governance and ethics, phases, informed consent and role of interpreters) using a blend of videos and presentations, interactive polls, and discussions. Pretraining and post-training surveys were conducted to assess learnings. Statistical analysis used descriptive statistics and t-tests.

resultsIn Phase 1, 133 interpreters responded to an initial online survey (response rate of 19%). The majority (71%) had been working as interpreters for more than 10 years. Clinical trial interpreting experience was limited; 34% had never interpreted for a clinical trial. Mean knowledge accuracy for clinical trial concepts was 68%, with uncertainty/lack of knowledge around randomization, clinical trial phases, and uncertainty around governance/ethics and clinical trial sponsors. In Phase 2, 92 interpreters attended in-person or online training. Training increased mean accuracy in knowledge items about cancer clinical trials from 74% prior to training to 91% after the training. Confidence in understanding clinical trial terminology increased from 20% to 62% after training.

conclusionTraining for HCIs improved knowledge and confidence in understanding cancer clinical trial principles and terminology, building competency to provide better service to people from CALD backgrounds. The training modules developed will be made available online for statewide interpreter access. Future evaluation should track the impact on CALD trial participation to assess long-term outcomes.

Indexed as

Clinical Trials as TopicCultural DiversityEthnicityNeoplasmsTranslatingAustraliaCommunication BarriersFemaleHumansMaleQuality ImprovementSurveys and Questionnairesclinical trialsculturally and linguistically diverse (CALD)education and trainingequitable outcomeethnic minoritieshealthcare interpreters

Identifiers

PMID40746202
PMCPMC12314417

What OpenQuestion holds

Textmetadata
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.