Evidence map›Paper›PMID 35191096›Full record

ArticleEuropean journal of cancer care2022

Not as simple as "fear of the unknown": A qualitative study exploring anxiety in the radiotherapy department.

Eliza Gimson, Maria Greca Dottori, Gemma Clunie, Caroline Yan Zheng, Theresa Wiseman, Elizabeth Joyce, Alison McGregor, Helen McNair

Open access · hybridAbstract read
In one paragraph

Article in European journal of cancer care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 22% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.

  1. Predictors of survival among head and neck cancer patients after radiotherapy based on health-related quality of life indices and symptoms-specific outcomes: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
    Pooled it
  2. Trial
  3. A machine learning approach to profiling anxiety risk among cancer patients on treatment.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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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

8 authors at 4 institutions in 1 country.

Eliza GimsonFaculty of Life Sciences and Medicine, King's College London, London, UK.ORCID https://orcid.org/0000-0003-3996-6243
Maria Greca DottoriFaculty of Medicine, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-7659-2124
Gemma ClunieFaculty of Medicine, Department of Surgery & Cancer, Imperial College London, London, UK.ORCID https://orcid.org/0000-0002-1796-731X
Caroline Yan ZhengInformation Experience Design & Fashion, Royal College of Art, London, UK.ORCID https://orcid.org/0000-0001-5277-3863
Theresa WisemanApplied Health Research, The Royal Marsden NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0002-3355-1269
Elizabeth JoyceThe Royal Marsden NHS Foundation Trust, London, UK.ORCID https://orcid.org/0000-0003-2784-613X
Alison McGregorMusculoskeletal Biodynamics, Imperial College London, London, UK.ORCID https://orcid.org/0000-0003-4672-332X
Helen McNairThe Royal Marsden NHS Foundation Trust, Translational Therapeutic Radiography, The Institute of Cancer Research, London, UK.ORCID https://orcid.org/0000-0001-7389-5587
Imperial College London · GBRoyal Marsden NHS Foundation Trust · GBKing's College London · GBRoyal College of Art · GB

Funding

CRUK Convergence Science Centre at The Institute of Cancer Research, London, and Imperial College London A26234National Institute for Health Research and Health Education England ICA-SCL-2018-04-ST2-002National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the NIHR Clinical Doctoral Research Fellowship Programme CDRF-2017-03-028/Integrated Clinical Academic ProgrammeNIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer ResearchNIHR Imperial Biomedical Research Centre
6 · The paper itself

Abstract

objectiveLittle is understood about the anxiety experienced by cancer patients undergoing radiotherapy or investigative imaging. Our aim was to identify sources of anxiety, the points along the cancer journey where anxiety occurred and methods to alleviate it.

methodsSix focus groups were conducted with cancer patients (n = 17), caregivers (n = 3) and healthcare practitioners (HCPs; n = 10) in the radiotherapy department. Patients described specific elements in the care pathway which induced anxiety, while HCPs focused on their perception of the patient experience. Thematic analysis was used to analyse data.

resultsThree broad themes emerged: The Environment, The Individual and The Unknown. The physical environment of the hospital, inside the scanner for example, emerged as a key source of anxiety. The impact of cancer on patients' individual lives was significant, with many feeling isolated. The majority of participants described anxiety associated with the unknown. HCPs reported difficulty in identifying the anxious patient.

conclusionsAnxiety is experienced throughout the cancer pathway. Common sources include the physical environment and the uncertainty associated with having cancer. Identifying both anxiety-inducing factors, and the anxious patients themselves, is crucial to enable targeted interventions to alleviate anxiety.

Indexed as

AnxietyCaregiversFearHumansPalliative CareQualitative Researchanxietycancerhealthcarequalitativeradiotherapyunknown

Identifiers

PMID35191096
PMCPMC10909434
OpenAlexW4213034855

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.