PLOS One | https://doi.org/10.1371/journal.pone.0329426 September 25, 2026 1 / 9

OPEN ACCESS
Citation: Galvez-Hernandez P, Audet L-A,
Shakeri Z, Wodchis WP (2026) Artificial
intelligence and machine learning for
organizational and system-level functions in
primary health care: A scoping review protocol.
PLoS One 21(9): e0329426. https://doi.
org/10.1371/journal.pone.0329426
Editor: Mohammad Saadati, Khoy University of
Medical Sciences, IRAN, ISLAMIC REPUBLIC OF
Received: July 16, 2025
Accepted: August 28, 2026
Published: September 25, 2026
Copyright: © 2026 Galvez-Hernandez 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 author and source are
credited.
Data availability statement: All relevant data
are within the manuscript and its Supporting
information files.
Funding: PGH’s and LA’s time was sup-
ported by a Postdoctoral Fellowship at the
STUDY PROTOCOL
Artificial intelligence and machine learning
for organizational and system-level functions in
primary health care: A scoping review protocol
Pablo Galvez-Hernandez 1,2*, Li-Anne Audet2, Zahra Shakeri2, Walter P. Wodchis2,3
1 Department of Health and Society, University of Toronto Scarborough, Toronto, Ontario, Canada,
2 Institute of Health Policy Management and Evaluation, Dalla Lana School of Public Health, University of
Toronto, Toronto, Ontario, Canada, 3 Institute for Better Health, Trillium Health Partners, Toronto, Ontario,
Canada

• pau.galvez@utoronto.ca
  Abstract
  Objective
  Primary health care (PHC) is the cornerstone of health systems worldwide, yet faces
  growing pressures from aging populations, workforce shortages, and constrained
  resources. While artificial intelligence (AI) and machine learning (ML) are increas-
  ingly used for clinical tasks such as diagnostics and decision support, their potential
  to address organizational and system-level processes remains underexplored. This
  scoping review maps AI and ML applications targeting non-clinical, organizational,
  and system-level functions in PHC, examining techniques employed, application
  purposes, data sources, and implementation maturity, and identifying evidence gaps
  to inform future research.
  Methods
  This scoping review will be conducted using the Arksey and O’Malley framework
  and reported according to PRISMA-ScR. We will search Ovid MEDLINE, EBSCO
  CINAHL, Ovid Embase, Cochrane Library (Wiley), Ovid PsycINFO, and IEEE Xplore,
  as well as grey literature sources, from January 2010 to December 2025, with a
  pre-submission update. Two independent reviewers will screen titles, abstracts, and
  full texts. Eligible studies include empirical research describing, evaluating, imple-
  menting, or developing AI and ML applications at the meso-level (organizational, e.g.,
  care scheduling, population stratification) and macro-level (system-wide, e.g., work-
  force planning, funding allocation) of PHC. Studies on micro-level clinical applica-
  tions, non-empirical research, and secondary literature will be excluded. Applications
  will be classified using a two-layer taxonomy (AI technique family and core algorithm)
  and mapped to PHC building blocks adapted from WHO operational frameworks.
  Synthesis will examine technique-building block intersections, application purposes,

PLOS One | https://doi.org/10.1371/journal.pone.0329426 September 25, 2026 2 / 9
data sources, implementation maturity, and evidence gaps. The protocol is registered
in Open Science Framework (osf.io/wzj5x).
Conclusions
This review will provide a timely synthesis of AI and ML applications at organizational
and system levels of PHC, identifying which building blocks have been addressed,
which remain underexplored, and where the field stands in terms of implementa-
tion readiness. The findings will inform a targeted research agenda and provide
actionable evidence for decision-makers seeking to leverage AI and ML for PHC
strengthening.
Background
Applications of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare
have grown exponentially over the past decade [1]. AI can be defined as a broad field
focused on systems that exhibit intelligent behavior by deriving knowledge from data
[2]. Machine learning (ML), commonly considered a subfield of AI, uses algorithms
to learn associations with predictive power from data examples through statistical
models [2]. These tools enable the analysis of large, complex data to uncover pat-
terns and predict outcomes, offering promising advancements in how healthcare is
delivered.
Most studies on AI and ML in healthcare have focused on their clinical appli-
cations, including diagnostic tools, chatbots, and predictive analytics embedded
in healthcare IT infrastructure for patient care delivery [3,4]. Additionally, there is
increasing interest in population-based applications in public health, often referred to
as precision population health management, which uses advanced analytics (e.g., AI,
machine learning, and predictive modeling) to stratify populations and tailor interven-
tions based on clinical, behavioral, and social data, aiming to deliver targeted, equita-
ble care at scale [5]. Numerous empirical data have shown that AI and ML can assist
clinicians to make better decisions, early diagnosis and patient monitoring [6,7].
These benefits have also been synthesized in systematic reviews, which highlighted
the advantages of AI and ML for patients, families, and healthcare professionals in
various healthcare contexts [8,9].
From a systems thinking perspective, effective health care delivery is not only a
function of clinical decision-making but is fundamentally shaped by the structural
and organizational processes that support it, including governance, financing, work-
force deployment, and service planning [10]. These meso-level (organizational and
team-level) and macro-level (regional, provincial, and national regulatory and funding
structures) dimensions constitute the infrastructure upon which clinical care operates
[11]. While AI and ML have demonstrated transformative potential for clinical tasks,
the capacity of these technologies to address the organizational and system-level
challenges that constrain health care delivery has received comparatively limited
attention in the literature.
University of Toronto, Institute of Health Policy,
Management and Evaluation, funded by the
Canadian Institutes of Health Research (CIHR)
(grant #513679) through the Optimizing Teams
for Interprofessional Care in Primary Health
Care (OPTIC-PHC) project, awarded under the
CIHR Project Grant program to WW. PGH also
received support from the Artificial Intelligence
for Public Health (AI4PH) Research Training
Platform. The funders had no role in the study
design, data collection and analysis, decision
to publish, or preparation of the manuscript.
No additional external funding was received for
this study.
Competing interests: The authors have
declared that no competing interests exist.

PLOS One | https://doi.org/10.1371/journal.pone.0329426 September 25, 2026 3 / 9
Within health systems, Primary Health Care (PHC), a cornerstone of efficient health systems and universal health cov-
erage, faces persistent challenges in sustaining high-quality service delivery [12]. Examples of structural and
process-related challenges include resource allocation favouring specialized care; difficulties in planning and funding PHC
strategies that involve multisectoral collaboration; adapting care models to local contexts; inadequate workforce planning
amid shortages; inefficiencies in payment and purchasing systems; and deploying integrated monitoring and evaluation
frameworks [12,13]. These challenges are compounded by an aging population with complex multimorbidity and growing
social needs, placing additional strain on PHC systems [14]. While economic and workforce resource constraints present
a major burden, existing infrastructures could be strengthened through more effective resource allocation, streamlined
care pathways, needs-based service and workforce planning, and better integration supported by timely data [15,16].
AI and ML might have the potential to support these improvements; however, their role in addressing these challenges
remains unclear and has not been comprehensively synthesized in existing reviews.
Some empirical research has explored the application of AI and ML beyond clinical encounters in primary care, including
population-level risk stratification [17], patient phenotyping and panel size optimization to improve provider workload distri-
bution [18], and appointment scheduling [19]. However, a comprehensive understanding of AI and ML applications across
the broader organizational and system-level dimensions of primary care remains absent, as existing reviews have focused
predominantly on micro-level applications involving individual patients, providers, and their clinical interactions. For exam-
ple, Abbasgholizadeh et al. mapped AI applications in community-based PHC and found that diagnosis, risk stratification,
disease surveillance, and clinical decision support were the dominant use cases, relying primarily on machine learning and
natural language processing [8]. These existing syntheses confirm the dominance of micro-level clinical applications in the
PHC AI/ML literature and underscore the need of reviews addressing the organizational and system-level domains targeted
by this protocol. While AI techniques are increasingly being applied to structural challenges such as strategic purchasing and
funding allocation in health systems [20], evidence of such applications within the PHC context specifically remains limited.
Addressing this gap is essential for understanding how AI and ML may support core PHC system functions, including
governance, resource allocation, workforce planning, and intersectoral coordination. This scoping review aims to: (i) map
and characterize current AI and ML applications in organizational and system-level dimensions of PHC; (ii) identify the
type of data sources and methods used to develop and apply AI/ML models; (iii) examine reported outcomes, strengths,
and limitations; and (iv) identify evidence gaps and future directions.
Methods
This scoping review follows the five stages proposed by Arksey and O’Malley: (i) identifying the research questions; (ii)
identifying relevant studies; (iii) study selection; (iv) charting the data; and (v) collating, summarizing and reporting the
results [21]. A scoping review was selected because it is designed to map the breadth of a heterogeneous evidence base,
identify key concepts, and clarify the boundaries of an emerging research area [21]. Results will be reported according to
the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist [22]. The analytical approach integrates a classifica-
tion taxonomy for AI/ML techniques that captures the type of methods employed and a set of PHC building blocks adapted
from WHO operational and measurement frameworks [12,15], which will enable a systematic cross-mapping of techniques
to PHC building blocks, which constitutes the primary analytical output of this review. This protocol has been registered in
Open Science Framework (OSF) (osf.io/wzj5x). In addition, a preprint version was posted on medRxiv through the jour-
nal’s facilitated preprint posting service during the manuscript submission process. Ethics approval was not required for
this study because human subjects are not involved.
Scoping review stages
Stage 1: Defining the research questions. The review will be guided by the following research questions: (i) What
are the current applications of AI and ML at the organizational and system-levels of PHC as reported in the literature? (ii)

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What types of data sources (e.g., electronic health records, administrative databases, national registries, survey data)
have been used to develop and apply AI and ML models in this context? (iii) What are the strengths and limitations of
these applications as reported by study authors? (iv) What gaps exist in the current evidence base in terms of underused
AI and ML techniques across PHC building blocks, and implementation maturity?
Stage 2: Identifying relevant studies. Studies will be searched in Ovid MEDLINE, EBSCO CINAHL, Ovid Embase,
Cochrane Library (Wiley), Ovid PsycINFO, and IEEE Xplore. A grey literature search will be conducted in OpenGrey,
Google Scholar, and ProQuest Dissertations & Theses Global. Reference lists of included articles and relevant literature
reviews will also be searched to identify additional studies [21]. The search will be restricted to English-language
publications from January 2010 to December 2025, with a final search update conducted prior to manuscript submission,
to ensure the review reflects the most current evidence available at the time of publication. The start date was chosen to
capture the period of rapid expansion of AI and ML in healthcare, marked by the widespread adoption of deep learning
techniques in the early 2010s [23].
A search strategy was iteratively developed following guidance from the Joanna Briggs Institute [ 24] and the
Cochrane Handbook for Systematic Reviews of Interventions [ 25]. The strategy was informed by preliminary searches,
comparison with search strategies from relevant published systematic and scoping reviews on AI/ML in health care,
and term analysis of five relevant articles identified through an initial limited search in MEDLINE (Ovid) using the
terms “artificial intelligence,” “machine learning,” and “primary health care.” Titles, abstracts, keywords, and index
terms from these articles were examined using the Yale MeSH Term Analyzer [ 26] to identify additional controlled
vocabulary and free-text terms. The resulting strategy combined keywords and index terms for two core concepts, AI/
ML and primary health care, using Boolean operators ( Table 1). It was then reviewed for sensitivity during consultation
with an expert librarian at the University of Toronto and adapted to the syntax and controlled vocabulary of each data -
base. The five hand-searched articles were used as a known-item check to confirm retrieval of relevant studies. The
term “data mining” was considered but not included as a separate term because of substantial overlap with machine
learning.
Stage 3: Study selection. All references will be uploaded to Rayyan software for deduplication and screening. Two
independent reviewers (PGH, LA) will conduct title, abstract, and full text screening using a detailed protocol outlining the
eligibility criteria. To validate the screening process, both reviewers will first simultaneously screen a sample of 25 articles
as a training exercise [21]. A calibration exercise will then be conducted, with both reviewers independently screening
20% of articles at each stage, targeting an inter-rater reliability above 80% [27]. Discrepancies will be resolved through
periodic meetings to discuss decision rationales, address inconsistencies, and reach consensus, with the involvement of a
third reviewer as tie-breaker when necessary.
The eligibility criteria are defined a priori, drawing from the conceptual definitions in Table 2. Inclusion criteria are: (i)
published and unpublished primary studies that (ii) describe, evaluate, implement, or develop AI/ML applications (iii) at the
organizational and system-levels of PHC systems; for example, clinic management, workforce planning, primary care pol-
icy analysis, or resource allocation across services. Unpublished primary studies include dissertations, theses, conference
papers and proceedings, and reports. AI methods in this review primarily refer to computational techniques that enable
learning of data-driven decision-making; knowledge-driven AI (e.g., expert systems) is also included where it appears
in the context of PHC systems. All categories and techniques of AI and ML are eligible (e.g., supervised, unsupervised,
semi-supervised, reinforcement learning, deep learning, natural language processing).
Exclusion criteria are: (i) non-empirical publications (e.g., commentaries, editorials); (ii) secondary studies such as liter-
ature reviews and meta-analyses; (iii) studies reporting micro-level AI and ML applications in PHC (e.g., diagnostic tools,
patient-facing digital technologies); (iv) studies conducted in settings other than PHC, such as clinical, public health, or
epidemiological research focused solely on the efficacy of technologies for individual patients or on measuring population
health profiles.

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Stage 4: Charting the data. The data charting process will involve a training phase, a reliability check, and full
extraction. A pilot test will be conducted on 10% of included studies to refine the charting form prior to full extraction. One
reviewer (PGH) will chart the data, which will be independently verified by a second reviewer (LA). Discrepancies will be
resolved through discussion or by a third reviewer if necessary.
The following information will be charted for each included study across four domains: (i) Study characteristics: title,
author(s), year of publication, country, source, study design, study objectives and funding source. (ii) AI/ML application:
AI technique family (Traditional ML, deep learning, NLP, or other AI/Hybrid); core algorithm(s) (the specific models used
for the main analytical task, e.g., random forest, logistic regression); ML learning approach (supervised, unsupervised,
semi-supervised, or reinforcement); application purpose (prediction, classification, clustering/segmentation, optimization,
monitoring/surveillance, decision support, or other); and primary data type (structured EHR/claims, unstructured text,
administrative data, survey data, images, or mixed). (iii) Primary system context: PHC system level (meso, macro, or
both); type of setting (e.g., primary care centers, community health centers); target population; and PHC building block, as
defined in Table 3. (iv) Outcomes and appraisal: implementation stage (development/validation only, pilot tested, or fully
deployed); main findings; and strengths and limitations of the AI/ML application as reported by the study authors.
Stage 5: Collating, summarizing, and reporting the results. Extracted data will be coded and categorized according
to the classification framework defined in Table 2 and mapped to the PHC building blocks defined in Table 3. To organize
the mapping of AI/ML applications to non-clinical PHC functions, we defined seven organizational and system-level
building blocks (Table 3), adapted from two WHO frameworks: the Operational Framework for Primary Health Care [12]
Table 1. Search strategy for Ovid MEDLINE.

Search terms

1 Artificial Intelligence/
2 Machine Learning/
3 Sentiment Analysis/
4 Deep Learning/
5 Natural Language Processing/
6 neural networks, computer/
7 Decision Support Systems, Clinical/
8 (artificial intelligence or machine learning or sentiment analysis or natural language processing or neural networks or deep learning or super-
vised learning or unsupervised learning or semi-supervised learning or reinforcement learning).tw,kf.
9 (expert system* or fuzzy logic or knowledge-based system* or knowledge based system* or Bayesian network* or large language model* or
generative artificial intelligence or generative AI or transformer model*).tw,kf.
10 (predictive model* or prediction model* or automated decision* or computer-aided or computer aided or computer-assisted or computer
assisted).tw,kf.
11 1 or 2 or 3 or 4 or 5 or 6 or 7 or 8 or 9 or 10
12 Primary Health Care/
13 Community Health Services/
14 Home Care Services/
15 Family Practice/
16 (primary care or primary healthcare or primary health care or primary practice* or general practice* or family practice* or ambulatory care or
community care or community health or home care).tw,kf.
17 ((primary or community or family) adj2 (health* or healthcare* or health care* or practice* or medicine)).tw,kf.
18 12 or 13 or 14 or 15 or 16 or 17
19 11 and 18
20 limit 19 to english
21 limit 20 to yr="2010 - 2025"
https://doi.org/10.1371/journal.pone.0329426.t001

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Table 2. Conceptual definitions underpinning eligibility criteria and the analytical classification framework.
Concept Categories and definitions
AI technique families For the purposes of classification and synthesis, AI techniques are grouped into four families based on their
primary analytical approach [28]: (i) Traditional ML: Algorithms used to classify and predict structured data; (ii)
Deep learning: Neural networks with multiple hidden layers to analyze high-dimensional patterns in large, com-
plex datasets; (iii) Natural language processing (NLP): Techniques for mining, extracting, or classifying informa-
tion from unstructured text.; (iv) Other AI/ Hybrid: Rule-based systems, expert systems, fuzzy logic, or mixed
approaches that do not fit the above categories.
Core algorithms The specific model(s) used for the main analytical task, such as logistic regression, random forest, support vector
machines, decision trees, k-nearest neighbors, gradient boosting, deep neural networks, k-means clustering, or
topic modelling [2,29]. Studies may employ multiple core algorithms.
Machine learning approaches ML algorithms are categorized by learning approach [28,29]: (i) Supervised learning: Algorithms that are trained
on labeled data to map inputs to outputs for classification or regression; (ii) Unsupervised learning: Algorithms
that analyze unlabeled data to uncover hidden patterns or structures.;(iii) Semi-supervised learning: Algorithms
trained on a small amount of labeled data combined with a larger pool of unlabeled data; (iv) Reinforcement
learning: algorithms that learn through interaction with an environment, using feedback (rewards or penalties) to
optimize actions.
Application purpose The functional objective of the AI/ML application, classified as: prediction, classification, clustering/segmentation,
optimization, monitoring/surveillance, decision support, or other.
Primary Health Care and Primary
Care
Primary health care (PHC): A comprehensive, society-wide strategy aimed at organizing and enhancing national
health systems to deliver health and well-being services closer to communities [30]. Primary care: The settings
that constitute the initial point of contact with the healthcare system, such as primary care centers, community
health centers, and community pharmacies [30].
Meso and macro levels of PHC
systems
Meso level: The organizational or local institutional level of PHC systems, including organizational dynamics,
team-level processes, and structural elements such as facility budgets and local intersectoral partnerships [31].
Macro level: Broader systemic influences, including national and provincial policies, scope of practice regulations,
and system-wide budgeting and resource allocation [31].
Note: Categories are not mutually exclusive (e.g., deep learning is frequently applied within NLP tasks); where overlap occurs, studies will be classified
by their primary technique and learning approach. These definitions inform both the inclusion/exclusion criteria for study selection and the classification
framework used during data extraction and synthesis.
https://doi.org/10.1371/journal.pone.0329426.t002
Table 3. Organizational and system-level building blocks of Primary Health Care, adapted from the WHO Operational Framework for PHC [12]
and the WHO Primary Health Care Measurement Framework [15].
PHC Building block Description and examples PHC Level
Workforce planning &
distribution
Predicting workforce needs, staff retention and turnover, rural and urban distribution,
workload and panel sizing, staff scheduling and allocation.
Macro/Meso
Funding and resource allocation Health system financing models, cost prediction, budget allocation, payment system
design, case-mix classification, efficiency analysis.
Macro/Meso
Service planning and demand
estimation
Forecasting service demand, estimating population-level service needs, planning
population-based interventions, home and community-based service planning.
Macro/Meso
Population stratification and risk
management
Risk stratification for care management, patient segmentation and complexity scor-
ing, panel management, referral optimization and triage, care prioritization.
Meso
Quality monitoring and safety Quality indicator extraction, patient safety incident classification, performance moni-
toring, clinical guideline adherence assessment, patient feedback analysis.
Macro/Meso
Digital health infrastructure and
data
Clinical data quality improvement, de-identification, medication and diagnostic cod-
ing, health information system development, teleconsultation classification.
Meso
Community engagement and
equity
Community health worker programs, social needs screening, health equity research,
social determinants of health integration, community outreach, intersectoral collabo-
ration planning.
Macro/Meso
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and the Primary Health Care Measurement Framework [15]. These frameworks identify structural and process-level
levers for strengthening PHC systems, including workforce deployment, financing, service planning, quality improvement,
digital health infrastructure, and community engagement. While the original frameworks encompass a broader set of PHC
dimensions, we consolidated those pertaining to organizational and system-level functions into seven building blocks that
capture the non-clinical domains to which AI/ML could be applied. Each study will be assigned one primary building block
based on the stated purpose of the AI/ML application, guided by the question: “What PHC function does this application
directly improve or enable?” Downstream or indirect effects will not be used for assignment.
Using this framework, extracted data will be synthesized in three components: First, a descriptive overview will present
temporal trends, geographic distribution, publication venues, PHC settings, and system levels. Second, the landscape
analysis will cross-tabulate AI technique families against PHC building blocks, presented as a heat map in which cell
size or color intensity reflects the number of studies and empty cells identify research gaps. Application purposes will be
mapped to building blocks with illustrative examples from included studies. Third, a methodological quality snapshot will
summarize across all included studies the proportion reporting a validation strategy, performance metrics, external valida-
tion, and use of publicly available data or code. Findings will be presented narratively, organized by PHC building block,
and supported by figures and summary tables. Author-reported strengths and limitations will be compiled, categorized
by building block, and analyzed thematically. A dedicated gap analysis section will identify underexplored PHC building
block-technique intersections and inform a targeted research agenda. The review process will be documented using
the PRISMA-ScR flow diagram and the PRISMA-P checklist (S1 File). Consistent with the aims of a scoping review of
mapping the breadth and nature of a research area rather than evaluate intervention effectiveness, a formal risk of bias
assessment will not be conducted [21].
Discussion
This scoping review protocol outlines a structured approach to mapping AI and ML applications at the organizational and
system levels of PHC. The review has completed Stage 3 (study selection), and completion of the full scoping review is
expected by July 2026. By synthesizing evidence from published and unpublished literature across six databases and
grey literature, the review will characterize how AI and ML have been applied to non-clinical PHC functions, including
workforce planning, resource allocation, service demand estimation, quality monitoring, and community engagement, and
identify where evidence is accumulating and where significant gaps remain. This focus extends beyond the predominantly
micro-level clinical applications explored in previous reviews [8,19], addressing a largely uncharted area in health services
and policy research.
The review is anticipated to make three contributions. First, the cross-tabulation of AI/ML technique families against
PHC building blocks will produce the first structured map of technique-area intersections at the organizational and system
levels of PHC, identifying both populated and empty cells that can directly inform a targeted research agenda. Second,
by classifying implementation stage and methodological approaches, the review will characterize the field’s readiness for
real-world deployment, which has direct relevance for health system decision-makers weighing AI/ML investments. Third,
the building blocks taxonomy and classification framework developed for this review may serve as reusable tools for future
syntheses in this domain.
Some limitations are anticipated in this scoping review. The restriction to English-language publications may exclude
relevant research from regions where PHC literature is published in local languages, particularly in Latin America, East
Asia, and Francophone Africa, where primary care systems may be organized differently and where AI/ML applications
to system-level challenges could yield distinct insights. Additionally, the use of specific “primary health care” terminol-
ogy may limit the capture of studies from contexts where equivalent first-level care services use different nomenclature.
Additionally, although the search strategy was developed iteratively with librarian consultation and informed by pub-
lished AI/ML search strategies, the absence of a librarian or information specialist as a co-author may have limited the

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comprehensiveness and reporting quality of the search methods, as librarian involvement has been associated with
higher-quality search reporting in knowledge synthesis studies [32]. Finally, this scoping review will prioritize breadth of
mapping over depth of outcome evaluation to identify what exists and where gaps lie, but will not assess the effectiveness
of individual AI/ML applications.
Despite these limitations, this review will provide a comprehensive and timely synthesis of AI and ML applications at
understudied levels of PHC. The findings will contribute to health services and policy research by identifying evidence
gaps, characterizing methodological patterns, and informing recommendations to strengthen the organizational and struc-
tural capacity of PHC systems.
Supporting information
S1 File. PRISMA-P checklist.
(DOCX)
Acknowledgments
The authors would like to thank the Faculty Liaison & Instruction Librarian at University of Toronto for their input when
designing and validating the search strategy.
Author contributions
Conceptualization: Pablo Galvez-Hernandez.
Methodology: Pablo Galvez-Hernandez, Li-Anne Audet, Zahra Shakeri, Walter P. Wodchis.
Supervision: Zahra Shakeri, Walter P. Wodchis.
Writing – original draft: Pablo Galvez-Hernandez.
Writing – review & editing: Pablo Galvez-Hernandez, Li-Anne Audet, Zahra Shakeri, Walter P. Wodchis.
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