Protocol for a Realist Review of Pathways to Lung Cancer Diagnosis in LMICs: A Focus on Contextual Factors and Application to the South African Healthcare System&nbsp;(ECLiPSA)

Jennifer Nyawira Githaiga; Conor F. Murphy; Joshua Graham; Sarah Day; Vedika Khurana; Raghad Khosaf; Oisin Brady Bates; Besigye Kabahena Innocent; Professor David Weakliam; Professor Bob Mash; Patrick Redmond

doi:10.12688/hrbopenres.14039.1

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Study Protocol

Protocol for a Realist Review of Pathways to Lung Cancer Diagnosis in LMICs: A Focus on Contextual Factors and Application to the South African Healthcare System (ECLiPSA)

[version 1; peer review: awaiting peer review]

Jennifer Nyawira Githaiga¹, Conor F. Murphy ², Joshua Graham², [...] Sarah Day³, Vedika Khurana², Raghad Khosaf², Oisin Brady Bates², Besigye Kabahena Innocent⁴, Professor David Weakliam⁵, Professor Bob Mash⁶, Patrick Redmond²

Jennifer Nyawira Githaiga¹, Conor F. Murphy ², [...] Joshua Graham², Sarah Day³, Vedika Khurana², Raghad Khosaf², Oisin Brady Bates², Besigye Kabahena Innocent⁴, Professor David Weakliam⁵, Professor Bob Mash⁶, Patrick Redmond²

PUBLISHED 04 Feb 2025

Author details Author details

¹ Division of Social and Behaviour Sciences, University of Cape Town Faculty of Health Sciences, Observatory, 7925, South Africa
² Department of General Practice, Royal College of Surgeons in Ireland, Dublin, Ireland
³ Cancer Research Iniative, University of Cape Town Faculty of Health Sciences, Observatory, Western Cape, South Africa
⁴ Family Medicine, Makerere University College of Health Sciences, Kampala, Uganda
⁵ Global Health Office, Health Service Executive, Dublin 8, Ireland
⁶ Family and Emergency Medicine, Stellenbosch University Faculty of Medicine and Health Sciences, Cape Town, South Africa

Jennifer Nyawira Githaiga
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Writing – Original Draft Preparation, Writing – Review & Editing

Conor F. Murphy
Roles: Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Writing – Review & Editing

Joshua Graham
Roles: Investigation, Project Administration, Validation, Writing – Review & Editing

Sarah Day
Roles: Conceptualization, Data Curation, Funding Acquisition, Investigation, Methodology, Project Administration, Writing – Original Draft Preparation, Writing – Review & Editing

Vedika Khurana
Roles: Investigation, Validation, Writing – Review & Editing

Raghad Khosaf
Roles: Investigation, Validation, Writing – Review & Editing

Oisin Brady Bates
Roles: Conceptualization, Methodology, Validation, Writing – Review & Editing

Besigye Kabahena Innocent
Roles: Conceptualization, Methodology, Validation

Professor David Weakliam
Roles: Conceptualization, Methodology, Validation

Professor Bob Mash
Roles: Conceptualization, Methodology, Validation

Patrick Redmond
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS AWAITING PEER REVIEW

Abstract

Background

Lung cancer is a leading cause of cancer-related mortality globally, with diagnostic delays in Low- and Middle-Income Countries (LMICs) often resulting in late-stage detection and poor outcomes. A comprehensive understanding of the pathways to diagnosis, and the contextual factors that shape them, is essential to improving early detection in these settings. This protocol describes a realist review to examine how patient, healthcare provider, and system-level contexts influence pathways to lung cancer diagnosis, focusing on barriers and facilitators.

Methods

The review will employ a realist approach to develop, test, and refine theories on how diagnostic pathways operate within LMIC contexts. Specifically, we will explore how contextual factors at the patient, healthcare provider, and system levels interact to influence mechanisms that lead to timely or delayed diagnosis. The initial programme theory (IPT) will be developed through a scoping review and stakeholder consultation, with iterative refinement as evidence is synthesised. Data from multiple sources, including peer-reviewed literature, grey literature, and stakeholder input, will be extracted, coded, and synthesised to identify context-mechanism-outcome (CMO) configurations. The review will adhere to RAMESES standards for realist reviews.

Outcomes

The review will generate CMO configurations explaining the function of lung cancer diagnostic pathways in LMICs, with a particular focus on South Africa as a case context. Findings will inform strategies and policies to improve early diagnosis by addressing key contextual barriers and enhancing facilitators. Stakeholder engagement will ensure findings are applicable and relevant to local healthcare settings.

Conclusion

This review aims to provide actionable insights into lung cancer diagnostic pathways in LMICs, enabling the development of targeted interventions to reduce delays, promote early diagnosis, and ultimately improve patient outcomes, with an emphasis on translating findings to South African healthcare contexts.

Keywords

Early detection of cancer, Lung neoplasms, Primary Health Care, Pathways to diagnosis, Resource-limited settings, Low and middle-income countries, Realist review.

Corresponding author: Conor F. Murphy

Competing interests: No competing interests were disclosed.

Grant information: Health Research Board [APA-2024-001].This work is also supported by Irish Research Council 23607A001 (COALESCE 2024).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2025 Nyawira Githaiga J et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Nyawira Githaiga J, Murphy CF, Graham J et al. Protocol for a Realist Review of Pathways to Lung Cancer Diagnosis in LMICs: A Focus on Contextual Factors and Application to the South African Healthcare System (ECLiPSA) [version 1; peer review: awaiting peer review]. HRB Open Res 2025, 8:25 (https://doi.org/10.12688/hrbopenres.14039.1) First published: 04 Feb 2025, 8:25 (https://doi.org/10.12688/hrbopenres.14039.1) Latest published: 04 Feb 2025, 8:25 (https://doi.org/10.12688/hrbopenres.14039.1)

Introduction

Lung cancer remains a leading cause of cancer-related morbidity and mortality worldwide, accounting for approximately 12.4% of all cancer deaths in 2022, when there were 2.4 new lung cancer cases with an age standardised incidence rate of 23.6/100000 and 1.8 million cancer deaths with an age standardised mortality rate of 16.8/100000¹. Early detection is critical to improving survival rates. However, in many Low- and Middle-Income Countries (LMICs), patients often present at advanced stages of the disease, limiting treatment options and contributing to poor outcomes^2–4. Factors such as socioeconomic disparities, limited access to healthcare, healthcare system inefficiencies, and cultural beliefs influence the ability to diagnose lung cancer early in LMIC settings^2–4.

In LMICs, the process of cancer diagnosis is particularly complex due to a multitude of context-specific challenges. These include limited geographic accessibility to health facilities, a shortage of adequately trained healthcare professionals, and variable healthcare infrastructure, which collectively impede timely and accurate diagnosis^2,5. Additionally, psychosocial and cultural factors, such as beliefs around cancer symptoms, stigma, and help-seeking behaviours, play a significant role in how and when individuals seek healthcare^3,6. Understanding these contextual influences is vital for developing strategies that can support earlier diagnosis and improve outcomes for lung cancer patients^3,7.

Unlike high-income countries (HICs), where lung cancer screening and diagnostic pathways are often streamlined and better resourced, LMICs face systemic and contextual barriers that require specific exploration to design and implement feasible interventions^2,4,7. Although there is an emerging body of literature on lung cancer diagnosis in LMICs, much of it is fragmented, often lacking a comprehensive framework that explains how pathways to diagnosis operate within their unique contextual realities.

A realist review approach is particularly well suited for synthesising complex evidence to produce context-specific explanations of how and why interventions work, for whom, and in what circumstances^8,9. This approach allows for a nuanced exploration of the "pathways to diagnosis" by focusing on the interaction between context, mechanisms, and outcomes (CMO)^8,10, ultimately developing theories that explain how diagnostic pathways unfold within diverse LMIC contexts. Such theories can then inform policies and practices to improve early detection efforts.

Lung cancer diagnosis in LMICs: challenges and context

The pathway to lung cancer diagnosis is shaped by factors at multiple levels, including patient, healthcare provider, and health system levels. At the patient level, factors such as knowledge of symptoms, socio-cultural beliefs, perceived stigma, and financial barriers to seeking care play a crucial role in whether an individual seeks medical attention for potential cancer symptoms^3,11. Evidence suggests that delays in symptom recognition or health-seeking behaviour, including ignoring or playing down symptoms, contribute significantly to late-stage lung cancer presentations in many LMICs^4,6.

At the provider level, challenges include limited training in cancer symptom recognition, lack of access to diagnostic tools, and low index of suspicion for lung cancer due to competing priorities in resource-limited settings^3,5. Moreover, the absence of standardised pathways for referral and investigation can lead to prolonged diagnostic intervals and missed opportunities for timely intervention^11,12.

At the health system level, structural issues such as healthcare financing, availability of diagnostic technologies (e.g., imaging facilities), and the organisation of primary and secondary care services critically impact the pathways to diagnosis^4,6. In many LMICs, health systems are fragmented, with inequitable access to care between urban and rural populations and between private and public sectors^13,14. These systemic factors often create "bottlenecks" in the diagnostic process, leading to avoidable delays in diagnosis.

South Africa, as an upper-middle-income country with significant health disparities, exemplifies many of these challenges. The dual public-private healthcare system, uneven resource distribution, and the high burden of infectious diseases (e.g., tuberculosis) that mimic lung cancer symptoms, complicate the diagnostic pathway for lung cancer patients^3,15. Lessons drawn from other LMICs can therefore be instrumental in guiding improvements in South Africa’s diagnostic pathways for lung cancer.

Realist review as a methodological approach

A realist review is distinct from other evidence synthesis methods due to its focus on understanding causality and the contextual factors that influence how interventions work or do not work^8,9. Traditional systematic reviews often seek to answer questions about the effectiveness of interventions; however, realist reviews aim to uncover the mechanisms through which interventions produce outcomes in particular contexts^8,16. This makes the realist review approach particularly suited for investigating complex interventions and processes, such as diagnostic pathways in healthcare^8,10.

Realist reviews use the CMO framework to understand how contexts (C) trigger specific mechanisms (M), leading to certain outcomes (O). In the case of lung cancer diagnosis in LMICs, this involves examining how various contextual factors (e.g., healthcare infrastructure, socio-cultural norms) influence mechanisms (e.g., patient help-seeking behaviours, provider decision-making) that ultimately affect outcomes (e.g., timeliness of diagnosis, accuracy of staging)^17,18.

The iterative nature of a realist review allows for the development of an initial programme theory (IPT), which is then refined through successive stages of evidence synthesis and stakeholder engagement^8,19. Stakeholder input is critical for validating findings and ensuring that emerging theories are grounded in real-world practice⁸. Stakeholder involvement will be integral to the review, as it enhances the relevance and applicability of findings. A group of expert stakeholders will be identified and engaged throughout the review process. The expert stakeholder group (ESG) will include health system experts, primary care physicians, researchers specialising in cancer diagnosis within LMIC settings, and patient advocacy representatives.

Aims

The primary aim of this realist review is to identify, synthesise, and theorise on the pathways to lung cancer diagnosis in LMICs, with a focus on understanding context-specific factors that influence these pathways. The review will generate insights applicable to the South African healthcare context, contributing to improved diagnostic pathways and early detection strategies.

Research questions

This review will be guided by the following research questions:

1. What are the key contextual factors influencing pathways to lung cancer diagnosis in LMICs?
2. How do mechanisms operating within these contexts impact the timeliness and accuracy of lung cancer diagnosis?
3. What are the context-mechanism-outcome (CMO) configurations that explain how pathways to lung cancer diagnosis function in LMICs?
4. How can insights from LMICs inform strategies to improve pathways to lung cancer diagnosis in South Africa?

Methods

Study design

A realist review will be conducted to understand and explain pathways to lung cancer diagnosis in Low- and Middle-Income Countries (LMICs). The approach focuses on identifying how context-specific factors influence mechanisms that lead to various outcomes in the diagnostic process. Unlike traditional systematic reviews that assess intervention effectiveness, a realist review seeks to develop context-mechanism-outcome (CMO) configurations to uncover how diagnostic pathways operate across different LMIC contexts. This review is guided by the five-step approach to conducting realist reviews adapted from Rycroft-Malone et al.⁹: 1) define the realist review scope, 2) search for evidence, 3) appraise evidence and extract data, 4) data synthesis, and 5) narrative development. Despite the fairly distinctive steps, the actual process of conducting a realist review is an iterative and dynamic one with movement between the various steps as part of the refinement process. A summary of the steps is outlined in Figure 1 and further, each step is described in subsequent paragraphs. This protocol follows the (Realist And Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES II) and the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) reporting standards to ensure methodological rigour and transparency throughout the review process^19,20.

Figure 1. Flow diagram of realist review process.

Step 1: define realist review scope

The realist review process started by identifying and refining the review question and scope of the review, which focuses on pathways to lung cancer diagnosis in LMICs. A key component of this phase is the formulation of an IPT, representing an early understanding of how lung cancer diagnosis pathways are expected to operate in LMICs. The IPT will be informed by initial scoping of the literature and consultation with the ESG. The ESG will participate in co-developing the IPT through sharing insights on barriers and facilitators to lung cancer diagnosis within LMICs, and contributing to an understanding of important contexts, mechanisms, and potential outcomes. This early theory will serve as a starting point for exploration and will be iteratively refined as new data is collected and synthesised. The IPT will provide the framework for mapping CMO configurations, which will evolve through the review process.

Step 2: search for evidence

This step will entail searching literature to identify papers that are relevant for testing and refining the ITPs selected in step 1. The search, which will be led by an experienced librarian, will commence with a pilot search using population-intervention-comparison-outcome (PICO) tool^21,22 in PubMed to refine the search strategy, including identification of relevant medical subjected headings (MeSH) using Boolean operators (Extended data1). Table 1 contains a list of PICO terms generated from the initial literature scope in step 1.

Table 1. PICO terms for realist review.

Population	Lung cancer OR lung neoplasms OR lung malignancy OR carcinoma OR lung tumour Lung* OR pulmo); (cancer OR neoplas* OR malignan* OR tumo* OR symptom* OR sign* “developing country” OR "Low and middle income countries" OR LMIC OR “emerging nations” OR “developing nations”
Intervention	diagnosis OR early detection OR screen* OR “symptom appraisal” OR campaign OR programme OR education OR “symptom awareness” OR intervention OR patient OR “health provider” OR “health system”
Comparison	“standard care” OR “usual care” OR “no intervention”
Outcomes	Pathway* OR help-seeking OR health-seeking OR access OR intentions OR behaviour OR strateg* OR model* OR efficacy OR effectiveness” OR uptake OR quality OR acceptability OR service provision OR service delivery OR healthcare cost OR cost effectiveness OR healthcare provision OR cost-effectiveness OR barrier

Information sources

Evidence will be obtained from various sources:

Electronic databases: The review will conduct searches in key medical and social science databases, including PubMed, Scopus, Web of Science and EBSCOhost (Africa-Wide Information, Cumulative Index to Nursing and Allied Health Literature (CINAHL). Search terms will include combinations of keywords related to "lung cancer," "diagnosis," "pathways," and LMIC-specific terms. The search strategy will be adjusted iteratively as concepts and relevant themes emerge during theory development. See extended data for an example search strategy
Grey literature: To capture unpublished or context-specific information not available in peer-reviewed journals, grey literature will be sourced from institutional reports, conference proceedings, theses, and government documents. This will help ensure that the review is inclusive of diverse sources that may offer insights into lung cancer diagnosis pathways in LMICs.

Additional evidence will be sought from forward and backward citation searching of reference lists from primary studies and systematic reviews as well as input from the review team and ESG members. We anticipate that there may further iterative searches conducted as needed during the review process. Even so, we acknowledge that the aim of the searches is not to be exhaustive but rather to purposively identify literature that will inform theory development.

Inclusion and exclusion criteria

To ensure the relevance and depth of data collected, the following criteria will be applied:

Inclusion criteria: Studies will be included if they explore pathways to lung cancer diagnosis within LMIC settings and provide data that contribute to understanding contextual, mechanistic, or outcome factors relevant to diagnosis. Both qualitative and quantitative studies will be eligible, as well as mixed-methods research.
Exclusion criteria: Studies focusing exclusively on high-income countries (HICs) will be excluded, as the aim is to develop theories relevant to LMICs. Additionally, studies that do not address context-specific factors influencing the diagnostic pathway will be excluded, as the realist approach prioritises an understanding of context-mechanism interactions. Studies published in languages other than English will be excluded from the study.

Step 3: appraise evidence and extract data

Screening and selection of studies will be done systematically following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations²³. Using Rayyan software, two independent reviewers will screen the titles and abstracts of studies that meet the inclusion criteria²⁴. Discrepancies will be resolved through discussion, with a third reviewer available to reach consensus if necessary. Full texts of potentially relevant studies will be assessed by the reviewers for inclusion in the review. A systematic record of reasons for exclusion will be kept maintaining transparency in the selection process.

Data will be extracted using a bespoke extraction form on an Excel spreadsheet, informed by the template for intervention description and replication (TIDieR) checklist²⁵ and the CMO framework (see supplementary material). Additional data will comprise annotations to identify context-mechanism-outcome (CMO) configurations based on IPTs using NVivo software^26,27. Extracted data will include:

• Contextual information: Characteristics of the country setting, health system structure, socio-cultural norms, and policy environment influencing diagnostic pathways.

• Mechanisms: Patient-level behaviours (e.g., help-seeking, symptom recognition), provider-level actions (e.g., clinical decision-making, referrals), and system-level processes (e.g., access to diagnostic tools, healthcare navigation).

• Outcomes: Key outcomes such as the timeliness of diagnosis, the stage of cancer at diagnosis, and delays at different stages of the diagnostic process will be recorded.

Quality appraisal of included studies will be determined based on whether or not the studies are useful for theory building or testing (relevance), and if the content is deemed credible (rigour)²⁸. Appraisal tools appropriate to the study design will be utilised for example CASP for qualitative studies²⁹ and MMAT for mixed methods studies³⁰. Rather than excluding studies based on quality alone, the appraisal will inform an understanding of the trustworthiness of evidence and its contribution to theory development. All studies, regardless of their methodological quality, will be evaluated for their potential to inform CMO configurations.

Step 4: data synthesis

The review will employ a theory-driven approach to analyse and synthesise data, following the iterative nature of realist methodology. The main focus in this step is to confirm, refute and refine CMO configurations in consultation with the ESG who will review emerging CMO configurations and to refine the developing programme theories in light of their expertise and practical experience. This iterative process allows for continuous theory development that is both grounded in evidence and responsive to real-world complexities.

Extracted data will be analysed to map the relationships between context, mechanisms, and outcomes. The IPT will guide the coding of data and the identification of patterns within and across studies. CMO configurations will be developed and refined as the understanding of how diagnostic pathways operate in LMICs deepens.

A cross-case synthesis will be undertaken to explore how similar or contrasting contexts influence mechanisms and outcomes in lung cancer diagnosis. This will enable the development of mid-range theories (MRTs) that explain how and why certain pathways produce timely or delayed diagnoses in specific LMIC contexts in step 5.

Step 5: develop narrative

This final step, which will be done in consultation with the ESG, tails reviewing final programme theories, developing mid-range theories and disseminating review findings, conclusions and recommendations. During the process leading up to the development of mid-range theories, CMO configurations will be refined through continuous synthesis of the literature and validation through ESG engagement. The aim is to produce nuanced explanations of how various contexts, such as rural versus urban settings or differences in socioeconomic status, impact mechanisms like patient help-seeking and provider decision-making, ultimately shaping diagnostic outcomes. As the review progresses to the stage of interpretation and application, stakeholders will assess the transferability of the CMO configurations to the South African healthcare context and other LMICs, helping to generate practical recommendations for improving diagnostic pathways.

Findings will be disseminated among ESG members in panel discussions to validate emerging theories and explore their applicability to South African primary care settings. This will include discussions on the transferability of findings to local practice and potential interventions for improving early lung cancer diagnosis. Dissemination will also include submission of a manuscript detailing the review results to a high-impact peer-reviewed journal.

Discussion

This realist synthesis aims to develop a theoretical understanding of how contextual factors influence pathways to lung cancer diagnosis in low- and middle-income countries (LMICs). Existing literature has highlighted systemic, provider, and patient-level challenges in these settings, including limited healthcare access, diagnostic delays, and socio-cultural barriers. However, a coherent framework explaining how these factors interact to shape diagnostic pathways remains underdeveloped. By using a realist approach, this review aims to address these gaps by developing nuanced context-mechanism-outcome (CMO) configurations that capture the complex interplay of influences on early lung cancer diagnosis in LMICs.

The strength of a realist approach lies in its ability to explore causal mechanisms within specific contexts, providing a deeper understanding of why and how diagnostic delays occur. This is particularly important in LMICs, where diverse healthcare systems, cultural norms, and economic disparities shape patient and provider behaviours differently compared to high-income countries (HICs). For example, prior studies have demonstrated that geographic barriers, limited diagnostic infrastructure, and overlapping symptoms with infectious diseases such as tuberculosis are critical factors in delayed lung cancer diagnosis in South Africa and other LMICs³. Understanding these factors within a structured theoretical framework allows for the identification of leverage points for intervention.

The findings of this review will inform the design of contextually appropriate interventions to improve lung cancer diagnosis in LMICs. Such interventions may include enhanced community-based symptom awareness campaigns, streamlined referral pathways, and capacity-building initiatives for healthcare workers. Furthermore, by applying the findings specifically to South Africa’s healthcare context, this review will provide actionable insights to address unique challenges such as the dual public-private health system and the high burden of tuberculosis, both of which contribute to diagnostic delays⁶.

Limitations

This review is subject to several limitations. First, the availability, quantity, and quality of evidence in LMICs may constrain the development of robust programme theories. Much of the existing literature on lung cancer diagnosis focuses on HICs, potentially limiting the transferability of insights. Second, the iterative nature of theory development in realist reviews, while essential for capturing complexity, may result in challenges with replicability. Third, the focus on studies published in English could exclude relevant evidence, particularly from francophone or lusophone African countries.

Additionally, while the context-specific nature of this review is a strength, it may limit generalisability beyond the targeted LMIC settings. Despite these limitations, the iterative synthesis process and stakeholder engagement will ensure that the resulting programme theories are grounded in diverse evidence and applicable to real-world healthcare systems.

Dissemination

The findings from this review will be shared through various channels, including academic journals, presentations at national and international conferences, and reports to relevant health and governmental organisations. Considering the focus of this review, we are especially committed to engaging the public and the communities impacted by lung cancer in low- and middle-income countries. Project findings will be distributed via established website and social media channels affiliated with the study team institutions and research groups, enhancing public and provider awareness.

Conclusion

In LMICs, lung cancer diagnostic pathways are shaped by a complex interplay of patient behaviours, provider practices, and systemic barriers. Delays in diagnosis contribute to late-stage presentations and poor outcomes, underlining the need for context-sensitive, evidence-based interventions. By synthesising existing evidence through a realist lens, this review will identify critical mechanisms driving delays and explore how these can be mitigated within specific healthcare contexts such as South Africa.

Ultimately, this work aims to generate actionable insights that inform policy and practice, improve patient navigation, and enhance the capacity of healthcare systems to achieve earlier diagnoses. Findings will contribute to the global effort to reduce disparities in lung cancer outcomes by addressing context-specific challenges and enabling targeted, sustainable interventions.

Data availability statement

No data are associated with this article.

Extended data statement

OSF: ECLiPSA: Protocol for a Realist Review of Pathways to Lung Cancer Diagnosis in LMICs, DOI: https://doi.org/10.17605/OSF.IO/U9QGC³¹

The project contains the following extended data:

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 

Copyright: © 2024 Githaiga J et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By Attribution 4.0 International), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reporting guidelines

OSF: ECLiPSA: Protocol for a Realist Review of Pathways to Lung Cancer Diagnosis in LMICs, DOI: https://doi.org/10.17605/OSF.IO/U9QGC³¹

ECLiPSA_Protocol_PRISMA-P_RAMESES-II-checklists.pdf

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 

Software availability statement

NVivo is not open access or free software. It operates on a licensing model. Taguette (https://www.taguette.org/) is a free alternative to NVivo.

Acknowledgments

Irish Research Council

Faculty Opinions recommended

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