Patient-, care partner-, and clinician-proposed solutions to address the time toxicity of cancer care
Bibliographic record
Abstract
PURPOSE: As the concept of time toxicity has gained traction in the oncology community, work has largely focused on mapping and measuring time burdens. This qualitative study sought to elicit perspectives from people with lived or professional experience with cancer care on strategies to decrease the time burden of oncology care. METHODS: We conducted semi-structured interviews with 47 participants (16 patients with advanced gastrointestinal cancer, 15 of informal care partners, and 16 oncology clinicians with diverse roles) from a single academic cancer center in Minneapolis, Minnesota. Interviews were conducted between February 2023 and October 2023 and transcribed and analyzed using a hybrid approach. RESULTS: Five key themes emerged as solutions to address time toxicity: (1) tailored ambulatory scheduling based on patient and care partner needs; (2) improved administrative and logistical support; (3) home-based care when safe, feasible, and preferred; (4) transparent and empathetic communication of time demands, and (5) innovative care models and delivery. Participants emphasized that the threshold between helpful and burdensome care in the context of a cancer diagnosis differs for each person, requiring individualized solutions. CONCLUSION: Findings from this study will aid the oncology community in considering and designing interventions to decrease the time burdens of cancer care on patients and their care partners. Implementing changes to meet the need for individualized, person-centered interventions, focusing on individuals' preferences and unique circumstances, will require health system will and motivation .
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".