The Experience of Patients with Cancer and Their Informal Caregivers Related to Adoptive Cell Therapy: A Qualitative Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Adoptive cell therapy (ACT) is a growing personalized immuno-oncology approach, delivered both in standard of care (SOC) and clinical trial (CT) settings. Understanding patient and informal caregivers (ICs) experiences is crucial to optimizing care. This qualitative systematic review explores the ACT experience across three elements: actors (patients and ICs), settings (CT and SOC), and phases of the care continuum. METHODS: A systematic search was conducted across Medline, Embase, CINAHL, APA PsycInfo, Cochrane, Web of Science, ProQuest Dissertations & Theses, and Google Scholar up to May 8, 2024. Studies were appraised using the JBI Critical Appraisal Checklist for Qualitative Research, with data extracted and synthesized using a meta-aggregation approach. MAXQDA was used to generate co-occurrence networks between key elements and inductively derived codes. A comparative sentiment analysis highlighted emotional differences between CT and SOC settings. RESULTS: Nineteen qualitative studies were included, capturing experiences of patients (n = 19) and ICs (n = 7) receiving chimeric antigen receptor T cell (n = 17) and tumor-infiltrating lymphocyte (n = 2) therapy in CT (n = 13) and SOC (n = 9) settings. Findings revealed phase-specific challenges across physical, cognitive, psychological, emotional, social, financial, professional, communication, and informational domains. These challenges originate from ACT-related toxicities, care pathway complexity, and the novel nature of the therapy. CONCLUSIONS: This review identifies the key challenges faced by patients and ICs throughout the ACT care pathway, emphasizing the need for tailored interventions based on the phase and setting, as well as improved support systems. IMPLICATIONS FOR NURSING PRACTICES: Recommended strategies include developing decision support tools, establishing caregiver support programs, and implementing navigation services to enhance patient and ICs experiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".