The role of time in involving patients with cancer in treatment decision making: A scoping review
Bibliographic record
Abstract
BACKGROUND: Time is often perceived as a barrier to shared decision making in cancer care. It remains unclear how time functions as a barrier and how it could be most effectively utilized. OBJECTIVE: This scoping review aimed to describe the role of time in patient involvement, and identify strategies to overcome time-related barriers. METHODS: Seven databases were searched for any publications on patient involvement in cancer treatment decisions, focusing on how time is used to involve patients, the association between time and patient involvement, and/or strategies to overcome time-related barriers. Reviewers worked independently and in duplicate to select publications and extract data. One coder thematically analyzed data, a second coder checked these analyses. RESULTS: The analysis of 26 eligible publications revealed four themes. Time was a resource 1) to process the diagnosis, 2) to obtain/process/consider information, 3) for patients and clinicians to spend together, and 4) for patient involvement in making decisions. DISCUSSION: Time is a resource throughout the treatment decision-making process, and generic strategies have been proposed to overcome time constraints. PRACTICE VALUE: Clinicians could co-create decision-making timelines with patients, spread decisions across several consultations, share written information with patients, and support healthcare redesigns that allocate the necessary time.
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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 | 0.026 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".