Decision Points Research Articles

Systemic family engagement to preserve and reunify families: Lessons from the U.S. and Chile

Family engagement is key to preserving and reunifying child-welfare involved families. Yet, despite legislation that requires child welfare agencies to make “reasonable efforts” in the United States (U.S.) and to restitute a child’s right to live with their family in Chile, there continue to be deficiencies to meaningfully engaging families. Via historical marginalization and exclusion of some groups, we argue that the state has created and perpetuated conditions that result in racial, socioeconomic, and geospatial disparities among child welfare decision-making points including substantiations, foster care entries, and reunifications. Using the U.S. and Chile as two case examples, we critically analyze the emergence and evolution of child welfare in conjunction with relevant institutional and systemic policies that disadvantage entire groups or communities and then continue to perpetuate emotional neglect once children are in care. Through this analysis, we argue that there is a need to address structural barriers that impede family engagement, preservation, and reunification, and structural factors that lead to the root causes of child maltreatment referrals. As more federal policies shift to preventing child maltreatment, we propose a multilevel, structural approach to identify community strengths and needs that could better support family preservation and reunification, and build community-wide resilience and wellbeing, while also advocating for structural changes to policies and practices that could enhance equity.

Neighborhood effects and consequences of criminal justice contact: a research framework

This paper proposes a framework to examine how neighborhood factors influence criminal justice (CJ) contact and contribute to disparities across multiple stages of the justice process. By conceptualizing the punishment process as a dynamic set of decision-making points, this study highlights the role of neighborhood context in shaping offenders’ CJ trajectories and post-CJ residential inequality. Using Harris County, Texas, as a case study, this research considers individual-, neighborhood-, and event-level variables to understand the cumulative effects of neighborhood characteristics on CJ outcomes. This study underscores the critical need to investigate neighborhood mobility and its broader implications for community development and public policy. The findings can be supported by extensive data from the Federal Statistical Research Data Centers and the Criminal Justice Administrative Records System, offering a robust analysis of offenders’ spatial patterns and economic transitions.

'Leaving the Door Open': Perspectives on Decision-Making for Non-Emergency Diabetes-Related Amputation.

Having a lower extremity amputation is a life-changing decision for people living with a diabetes-related foot ulcer. Although previous research has described both positive and negative lifestyle and function outcomes of diabetes-related amputations, limited research has been conducted on the decision-making processes leading up to the amputation. This study aimed to explore the perspectives of persons, healthcare practitioners and experts (including academics and specialists) on decision-making for people with a diabetes-related foot ulcer who may require a non-emergency amputation. A qualitative descriptive study using semi-structured interviews enabled people to share their thought processes when making decisions for amputation. Twenty-six participants were interviewed, including nine people with a diabetes-related foot ulcer or amputation, nine health practitioners and eight experts located across five countries. There were 13 female and 13 male participants. Thematic analysis was used for data analysis. Four themes described the decision-making considerations for amputation: 'Balancing the evidence in decision-making', 'Trust, respect and timing of conversations inform decision-making', 'Tailoring decisions for individual circumstance' and 'Reaching the tipping point in decisions for the future'. Work commitments, functional and lifestyle impacts of amputation, the presence of support networks and clinical wound features formed the evidence for a decision for amputation. Understanding quality of life needs ensured that decisions for amputation addressed expectations and lifestyle needs. Living with a diabetes-related foot ulcer presented daily challenges that pushed people to a tipping point, at which amputation was considered to overcome these hardships and enable them to move on to the next chapter of their life. Further research is required to understand how person-centred factors can be better incorporated alongside objective clinical assessments in decisions for amputation. People with diabetes-related foot ulcers, health practitioners and experts shared their perspectives on the decision-making process for amputation through one-to-one interviews. Consideration of the person in the context of their life, environment and personal needs alongside the pathological factors is warranted.

Reexploring the conception of heat-health risk: From the perspectives of dimensionality and spatiality.

Extreme heat events are more frequent and intense as a result of global climate change, thus posing tremendous threats to public health. However, extant literature exploring the multidimensional features of heat-health risks from a spatial perspective is limited. This study revisits extreme heat-health risk and decomposes this concept by integrating multi-sourced datasets, identifying compositional features, examining spatial patterns, and comparing classified characteristics based on local conditions. Using Maryland as the focal point, we found that the components of heat-health risk are different from traditional risk dimensions (i.e., vulnerability, hazards, and exposure). Through a local-level clustering analysis, heat-health risks were compared with areas having similar features, and among those with different features. The findings suggest a new perspective for understanding the socio-environmental and socio-spatial features of heat-health risks. They also offer an apt example of applying cross-disciplinary methods and tools for investigating an ever-changing phenomenon. Moreover, the spatial classification mechanism provides insights about the underlying causes of heat-health risk disparities and offers reference points for decision-makers regarding identification of vulnerable areas, resource allocation, and causal inferences when planning for and managing extreme heat disasters.

Plastics in the Indian economy: a comprehensive material flow analysis

AbstractPlastic is valued for its flexibility to be utilized in different applications, yet it poses a significant threat to our environment because of mismanaged plastic waste. India’s compound annual growth of plastic consumption has been around 7% for a decade. Despite this significant growth, there has not been a comprehensive study of Indian plastic flows since 2000. This work presents a 20-year update, detailing plastic production, consumption by all plastic types and sectors, and the overall material flow for 2018–19 to fill the gap in the data on post-consumer plastic flows. The analysis reveals a total plastic production of 19.3 Mt, 22% of which is Polyethylene as the most wildly used plastic. The total mass of plastic in products distributed in various applications is 23.9 Mt. Key sectors for plastic consumption are Packaging (30%), Textiles (17%), and Buildings and Construction (16%). Plastic waste generation is 15.5 Mt, primarily from packaging and textiles. Only 13% of this plastic gets recycled, 46% is mismanaged, and the rest incinerated or dumped. The study’s unique nationwide, mass-balanced, transparent approach offers a rigorous reference point for decision-makers. Yet, the lack of reliable data is the main barrier to design, implement, and monitor of policy interventions.

Emotional and psychological safety in the context of digital transformation in healthcare: a mixed-method strategic foresight study

BackgroundPerceived safety has received attention in the digital transformation of healthcare. However, the impact of perceived safety on the future of digital transformation has not been fully elucidated.AimTo investigate perceived...

Perceptions of biodiversity loss among future decision-makers in 37 countries

The decline of global biodiversity is a major environmental issue with far-reaching consequences for humans and the Earth System. When it comes to biodiversity conservation, university students play an important role because, as future decision makers, they will have an important influence on how society deals with biodiversity loss. Until now, there has been no international research examining how these future decision-makers in society perceive the causes of biodiversity loss. Using a recent method customized for this data, we show here that there are eight distinct response types across the 37 countries studied that differ in their perceptions of the drivers of biodiversity loss. In one of these response types, climate change was underestimated, while in others pollution or invasive species were rated substantially lower compared to the other main drivers. The distribution of the eight response types varied between the countries. Our results demonstrate how future decision-makers around the world evaluate the drivers of biodiversity loss. Country-specific conditions and differences between the surveyed countries were revealed. The findings serve as a starting point for decision-makers around the world to tailor education programs and policy measurements to the circumstances in their countries.

Understanding post-hospitalised patients' experiences of long-COVID - the PELCO study.

Despite significant advances in long COVID research, many aspects of the condition remain unknown. There is a persisting need for further research to improve the management of long COVID symptoms. This study aimed to explore the experiences and psychological needs of patients who were previously hospitalised with COVID-19, and who subsequently developed long COVID symptoms. Twelve patients with long COVID were interviewed between October 2021 and June 2022. Transcripts were analysed thematically. An overarching theme of 'Existential Crisis' was developed, incorporating three interconnecting sub-themes: 'Facing Psychological Threat', 'Seeking Legitimisation' and 'Forging a Path Through Uncertainty'. Findings suggest that the psychological impact of emergency hospitalisation for COVID-19 can be severe, particularly for those with ongoing long COVID symptoms, and that early psychological intervention should be available. Our findings also suggest the importance of further planning for future pandemics to ensure the presence of patient advocates during hospitalisation at points of critical decision-making.

Perspektif Al-Qur’an Tentang Sel Saraf Dalam Kajian Integrasi Agama Dan Sains

Humans are God's perfect creation. Perfection lies in reason. Apart from intelligence, humans also have a brain as the control center for all human activities. The aim of this research is to examine and discover more deeply about human nerve cells from the perspective of the Koran. This research uses qualitative methods and produces descriptive data through a literature approach. The main sources start from secondary sources including books or journals related to the concept of nerve cells according to religious and scientific views. There are several verses scattered in the surahs of the Koran regarding nerve cells. The word "Naashiyah" in this verse means forelock. Scientific facts about the crown is the part of the forebrain that functions as a controller, memory and decision maker, which is known as Frontal lobe. The information from these verses is true that the forelock is the place where decisions are made and is the measuring point for right or wrong decisions taken. Therefore, Al-Quran information and scientific facts from research results are integrated with each other and show harmony.

PPB-MCTS: A novel distributed-memory parallel partial-backpropagation Monte Carlo tree search algorithm

Monte-Carlo Tree Search (MCTS) is an adaptive and heuristic tree-search algorithm designed to uncover sub-optimal actions at each decision-making point. This method progressively constructs a search tree by gathering samples throughout its execution. Predominantly applied within the realm of gaming, MCTS has exhibited exceptional achievements. Additionally, it has displayed promising outcomes when employed to solve NP-hard combinatorial optimization problems. MCTS has been adapted for distributed-memory parallel platforms. The primary challenges associated with distributed-memory parallel MCTS are the substantial communication overhead and the necessity to balance the computational load among various processes. In this work, we introduce a novel distributed-memory parallel MCTS algorithm with partial backpropagations, referred to as Parallel Partial-Backpropagation MCTS (PPB-MCTS). Our design approach aims to significantly reduce the communication overhead while maintaining, or even slightly improving, the performance in the context of combinatorial optimization problems. To address the communication overhead challenge, we propose a strategy involving transmitting an additional backpropagation message. This strategy avoids attaching an information table to the communication messages exchanged by the processes, thus reducing the communication overhead. Furthermore, this approach contributes to enhancing the decision-making accuracy during the selection phase. The load balancing issue is also effectively addressed by implementing a shared transposition table among the parallel processes. Furthermore, we introduce two primary methods for managing duplicate states within distributed-memory parallel MCTS, drawing upon techniques utilized in addressing duplicate states within sequential MCTS. Duplicate states can transform the conventional search tree into a Directed Acyclic Graph (DAG). To evaluate the performance of our proposed parallel algorithm, we conduct an extensive series of experiments on solving instances of the Job-Shop Scheduling Problem (JSSP) and the Weighted Set-Cover Problem (WSCP). These problems are recognized for their complexity and classified as NP-hard combinatorial optimization problems with considerable relevance within industrial applications. The experiments are performed on a cluster of computers with many cores. The empirical results highlight the enhanced scalability of our algorithm compared to that of the existing distributed-memory parallel MCTS algorithms. As the number of processes increases, our algorithm demonstrates increased rollout efficiency while maintaining an improved load balance across processes.

Healthcare system barriers impacting the care of Canadians with myalgic encephalomyelitis: A scoping review.

Myalgic encephalomyelitis (ME,also known as chronic fatigue syndrome or ME/CFS)is a debilitating, complex, multisystem illness. Developing a comprehensive understanding of the multiple and interconnected barriers to optimal care will help advance strategies and care models to improve quality of life for people living with ME in Canada. To: (1) identify and systematically map the available evidence; (2) investigate the design and conduct of research; (3) identify and categorize key characteristics; and (4) identify and analyse knowledge gaps related to healthcare system barriers for people living with ME in Canada. The protocol was preregistered in July 2022. Peer-reviewed and grey literature was searched, and patient partners retrieved additional records. Eligible records were Canadian, included people with ME/CFSand included data or synthesis relevant to healthcare system barriers. In total, 1821 records were identified, 406 were reviewed in full, and 21 were included. Healthcare system barriers arose from an underlying lack of consensus and research on ME and ME care; the impact of long-standing stigma, disbelief, and sexism; inadequate or inconsistent healthcare provider education and training on ME; and the heterogeneity of care coordinated by family physicians. People living with ME in Canada face significant barriers to care, though this has received relatively limited attention. This synthesis, which points to several areas for future research, can be used as a starting point for researchers, healthcare providers and decision-makers who are new to the area or encountering ME more frequently due to the COVID-19 pandemic.

Comparing Methods for Identifying Post-Market Patient Preferences at the Point of Decision-Making: Insights from Patients with Chronic Pain Considering a Spinal Cord Stimulator Device.

To compare three methods for identifying patient preferences (MIPPs) at the point of decision-making: analysis of video-recorded patient-clinician encounters, post-encounter interviews, and post-encounter surveys. For the decision of whether to use a spinal cord stimulator device (SCS), a video coding scheme, interview guide, and patient survey were iteratively developed with 30 SCS decision-making encounters in a tertiary academic medical center pain clinic. Burke's grammar of motives was used to classify the attributed source or justification for a potential preference for each preference block. To compare the MIPPs, 13 patients' encounters with their clinician were video recorded and subsequently analyzed by 4 coders using the final video coding scheme. Six of these patients were interviewed, and 7 surveyed, immediately following their encounters. For videos, an average of 66 (range 33-106) sets of utterances potentially indicating a patient preference (a preference block), surveys 33 (range 32-34), and interviews 25 (range 18-30) were identified. Thirty-eight unique themes (75 subthemes), each a preference topic, were identified from videos, surveys 19 themes (12 subthemes), and interviews 39 themes (54 subthemes). The proportion of preference blocks that were judged as expressing a preference that was clearly important to the patient or affected their decision was highest for interviews (72.8%), surveys (68.0%), and videos (27.0%). Videos mostly attributed preferences to the patient's situation (scene) (65%); interviews, the act of receiving or living with SCS (43%); surveys, the purpose of SCS (40%). MIPPs vary in the type of preferences identified and the clarity of expressed preferences in their data sets. The choice of which MIPP to use depends on projects' goals and resources, recognizing that the choice of MIPP may affect which preferences are found.

Discovery of sulfonamide resistance genes in deep groundwater below Patna, India

Global usage of pharmaceuticals has led to the proliferation of bacteria that are resistant to antimicrobial treatments, creating a substantial public health challenge. Here, we investigate the emergence of sulfonamide resistance genes in groundwater and surface water in Patna, a rapidly developing city in Bihar, India. We report the first quantification of three sulfonamide resistance genes (sulI, sulII and sulIII) in groundwater (12–107 m in depth) in India. The mean relative abundance of gene copies was found to be sulI (2.4 × 10−2 copies/16S rRNA gene) > sulII (5.4 × 10−3 copies/16S rRNA gene) > sulIII (2.4 × 10−3 copies/16S rRNA gene) in groundwater (n = 15) and surface water (n = 3). A comparison between antimicrobial resistance (AMR) genes and wastewater indicators, particularly tryptophan:fulvic-like fluorescence, suggests that wastewater was associated with AMR gene prevalence. Urban drainage channels, containing hospital and domestic wastes, are likely a substantial source of antimicrobial resistance in groundwater and surface water, including the Ganges (Ganga) River. This study is a reference point for decision-makers in the fight against antimicrobial resistance because it quantifies and determines potential sources of AMR genes in Indian groundwater.

CODAS–Hamming–Mahalanobis Method for Hierarchizing Green Energy Indicators and a Linearity Factor for Relevant Factors’ Prediction through Enterprises’ Opinions

As enterprises look forward to new market share and supply chain opportunities, innovative strategies and sustainable manufacturing play important roles for micro-, small, and mid-sized enterprises worldwide. Sustainable manufacturing is one of the practices aimed towards deploying green energy initiatives to ease climate change, presenting three main pillars—economic, social, and environmental. The issue of how to reach sustainability goals within the sustainable manufacturing of pillars is a less-researched area. This paper’s main purpose and novelty is two-fold. First, it aims to provide a hierarchy of the green energy indicators and their measurements through a multi-criteria decision-making point of view to implement them as an alliance strategy towards sustainable manufacturing. Moreover, we aim to provide researchers and practitioners with a forecasting method to re-prioritize green energy indicators through a linearity factor model. The CODAS–Hamming–Mahalanobis method is used to obtain preference scores and rankings from a 50-item list. The resulting top 10 list shows that enterprises defined nine items within the economic pillar as more important and one item on the environmental pillar; items from the social pillar were less important. The implication for MSMEs within the manufacturing sector represents an opportunity to work with decision makers to deploy specific initiatives towards sustainable manufacturing, focused on profit and welfare while taking care of natural resources. In addition, we propose a continuous predictive analysis method, the linearity factor model, as a tool for new enterprises to seek a green energy hierarchy according to their individual needs. The resulting hierarchy using the predictive analysis model presented changes in the items’ order, but it remained within the same two sustainable manufacturing pillars: economic and environmental.

Legislating for Good Governance in the Pharmaceutical Sector through UN Convention Against Corruption (UNCAC) Compliance

ABSTRACT Pharmaceutical sector corruption undermines patient access to medicines by diverting public funds for private gain and exacerbating health inequities. This paper presents an analysis of UN Convention Against Corruption (UNCAC) compliance in seven countries and examines how full UNCAC adoption may reduce corruption risks within four key pharmaceutical decision-making points: product approval, formulary selection, procurement, and dispensing. Countries were selected based on their participation in the Medicines Transparency Alliance and the WHO Good Governance for Medicines Programme. Each country’s domestic anti-corruption laws and policies were catalogued and analysed to evaluate their implementation of select UNCAC Articles relevant to the pharmaceutical sector. Countries displayed high compliance with UNCAC provisions on procurement and the recognition of most public sector corruption offences. However, several countries do not penalise private sector bribery or provide statutory protection to whistleblowers or witnesses in corruption proceedings, suggesting that private sector pharmaceutical dispensing may be a decision-making point particularly vulnerable to corruption. Fully implementing the UNCAC is a meaningful first step that countries can take reduce pharmaceutical sector corruption. However, without broader commitment to cultures of transparency and institutional integrity, corruption legislation alone is likely insufficient to ensure long-term, sustainable pharmaceutical sector good governance.

Histopathological correlation of brain tumor recurrence vs. radiation effect post-radiosurgery as detected by MRI contrast clearance analysis: a validation study.

The differentiation between adverse radiation effects (ARE) and tumor recurrence or progression (TRP) is a major decision-making point in the follow-up of patients with brain tumors. The advent of immunotherapy, targeted therapy and radiosurgery has made this distinction difficult to achieve in several clinical situations. Contrast clearance analysis (CCA) is a useful technique that can inform clinical decisions but has so far only been histologically validated in the context of high-grade gliomas. This is a series of 7 patients, treated between 2018 and 2023, for various brain pathologies including brain metastasis, atypical meningioma, and high-grade glioma. MRI with contrast clearance analysis was used to inform clinical decisions and patients underwent surgical resection as indicated. The histopathology findings were compared with the CCA findings in all cases. All seven patients had been treated with gamma knife radiosurgery and were followed up with periodic MR imaging. All patients underwent CCA when the necessity to distinguish tumor recurrence from radiation necrosis arose, and subsequently underwent surgery as indicated. Concordance of CCA findings with histological findings was found in all cases (100%). Based on prior studies on GBM and the surgical findings in our series, delayed contrast extravasation MRI findings correlate well with histopathology across a wide spectrum of brain tumor pathologies. CCA can provide a quick diagnosis and have a direct impact on patients' treatment and outcomes.

A Survey of Kidney Cancer Analysis Using Machine Learning and Deep Learning Algorithms

A recent and emerging public health concern is kidney disease, particularly Chronic Kidney Disease (CKD), characterized by a gradual decline in kidney function due to various ailments. Kidney cancer, a potentially fatal form of cancer, requires accurate staging for effective treatment selection, with Stage 1 being a crucial point for decisions. Physicians often face challenges in accurately determining cancer stages, leading to potential under- or over-treatment. Kidneys play a vital role in filtering blood, removing waste and excess fluids, excreted as urine. Advanced renal disease can cause dangerous build-ups of fluids, electrolytes, and waste. In early stages, kidney disease may exhibit minimal symptoms, often progressing unnoticed until more advanced stages. Kidney cancer arises when kidney cells undergo abnormal growth and lose control. Treatments for kidney cancer encompass a range of options, including surgical procedures, chemotherapy, and radiation therapy. Early detection remains pivotal for effective intervention, echoing the universal importance seen in managing all types of cancer. In contrast, managing chronic kidney disease focuses on minimizing kidney damage by targeting its root causes, although addressing these factors may not always halt the disease's progression. In advanced stages, kidney cancer can potentially lead to kidney failure, a life-threatening condition necessitating artificial filtration methods like hemodialysis or surgical interventions for survival. “Various risk factors encompassing dietary habits, environmental exposures, and living conditions contribute significantly to sudden and often unnoticed illnesses among many individuals. Physicians commonly encounter challenges in accurately determining the stages of cancer, potentially leading to either under treatment or overtreatment. The diagnosis process for chronic renal disease is often fraught with risks, high costs, time-intensive procedures, and invasiveness. Consequently, numerous illnesses remain untreated until they reach advanced stages, especially prevalent in regions with limited resources, highlighting disparities in healthcare accessibility and early detection. Early detection is vital for doctors to save lives. Detecting kidney conditions early can help prevent kidney damage and slow down Kidney Cancer progression. “Researchers are exploring various methods to identify kidney disease at an early stage. This research aims to understand and improve early prediction techniques for this condition. To categorize Kidney cancer, researchers use different classification strategies like Artificial Neural Networks (ANN), Naive Bayes, Support Vector Machines (SVM), Adaptive Deep Convolutional Neural Network (AHDCNN), Alex Net, U-Net 2D, ada boost classifiers, and Linear Discriminant methods.

Seasonal climate forecast-an important tool in managing the risk of extreme weather events in Australia's wheat industry

This study aims to investigate the benefits of using the Australian Community Climate and Earth-System Simulator-Seasonal Version 2 (ACCESS-S2), a state-of-the-art seasonal climate forecast (SCF) system, to improve contingent decision-making in the Australian wheat industry. Six locations in eastern Australia, three cultivars with varying maturities, and 17 times of sowing (TOS) were considered. Seasonal hindcasts from ACCESS-S2, initialized on the 1st May for the period 1981–2018 were linked with the Agricultural Production System sIMulator (APSIM)-Wheat model (v7.10) to (1) assess the hindcast skill of ACCESS-S2 in predicting the occurrence of extreme weather events and in predicting above/below long-term median wheat yield; (2) identify optimal management strategies in mitigating the impact of extreme weather events; and (3) quantify the yield benefit of using ACCESS-S2 at the key decision-making point of sowing against a baseline management situation (i.e., without the use of SCF). Research results showed that (1) ACCESS-S2 has varying skills in predicting extreme climate indices and wheat yield across the six locations at a seasonal lead time; (2) 68 % of cases had a yield gain using SCF information; (3) across all cases, there was an average yield gain of 281 kg/ha representing an increase of 13 %; (4) benefits of using SCF were seen across predicted wet years, neutral years and dry years accounting for 69 %, 65 % and 72 % respectively, and (5) the identified optimal management options varied from location to location with earlier sowing associated with either suntop or gregory or both being the optimal strategies at most of the locations considered. Overall, there is a demonstrated benefit in utilizing ACCESS-S2 forecasts in the Australian wheat industry to improve farm management decision-making. The benefit can occur in any climate state but with dry years being more likely and significant (with an average yield increase of 97 % across the locations).

Understanding Farmers' Data Collection Practices on Small-to-Medium Farms for the Design of Future Farm Management Information Systems

Farm Management Information Systems (FMIS) integrate data from a variety of sources, including sensors, for the purpose of enabling farmers to interpret past activity and predict future performance. FMIS is traditionally designed for and used by large farms, given their capital and need for automation and scale-up. This paper examines the current data collection practices on small and medium farms so that FMIS systems can be better designed to their needs. Our empirical research comprises interviews conducted during 10 farm visits. Our semi-structured interviews incorporated questions about daily activities, points of decision-making, data sharing, and incentives for data collection. We analyzed the interviews by focusing on possible obstacles to adopting expanding digital data collection practices and how expanded data collection might help fulfill farmers' goals and motivations. We found that farmers use their own bespoke data collection techniques instead of or in parallel to more formalized methods and often hold key observations and hypotheses in their heads rather than committing them to any data collection system at all. Key barriers to FMIS adoption include technology skepticism, technical hurdles, lack of support, and self-doubt in technical skills. Based on this empirical work and analysis, we recommend that FMIS systems can best address the needs of small and medium farms by 1) accounting for the farmers' different approaches to memorizing vs. storing data, 2) integrating rather than trying to replace existing practices, and 3) considering the economic and political motivations driving farm decision-making and practices.

“Like not having an arm”: a qualitative study of the impact of visitor restrictions on cancer care during the COVID-19 pandemic

PurposeVisitor restriction policies to prevent the spread of COVID-19 among patients and clinicians were widespread during the pandemic, resulting in the exclusion of caregivers at key points of cancer care and treatment decision-making. The aim of this study was to explore how visitor restrictions impacted cancer treatment decision-making and care from patient and physician perspectives.MethodsSixty-seven interviews, including 48 cancer patients and 19 cancer and palliative care physicians from four academic cancer centers in the USA between August 2020 and July 2021.ResultsVisitor restrictions that prevented caregivers from participating in clinic appointments and perioperative hospital care created challenges in cancer care that spanned three domains: practical, social, and informational. We identified eight themes that characterized challenges within the three domains across all three groups, and that these challenges had negative emotional and psychological consequences for both groups. Physicians perceived that patients’ negative experiences due to lack of support through the physical presence of caregivers may have worsened patient outcomes.ConclusionsOur data demonstrate the tripartite structure of the therapeutic relationship in cancer care with caregivers providing critical support in the decision-making and care process to both patients and physicians. Caregiver absences led to practical, psychosocial, and informational burdens on both groups, and likely increased the risk of burnout among physicians. Our findings suggest that the quality of cancer care can be enhanced by engaging caregivers and promoting their physical presence during clinical encounters.