Evaluating the acceptability of a self-directed, self-management intervention for patients and caregivers facing advanced cancer
Bibliographic record
Abstract
Abstract Objectives Coping-Together is a self-directed, self-management intervention initially developed for patients in early-stages of cancer and their caregivers. This study evaluated its acceptability among patients with advanced cancer and their caregivers. Methods Twenty-six participants (patients with advanced cancer n = 15 and their caregivers n = 11) were given the Coping-Together materials (6 booklets and a workbook) for 7 weeks. Participants were interviewed twice during this time to solicit feedback on the intervention’s content, design, and recommended changes. Audio-recorded interviews were transcribed verbatim, and thematic analysis was conducted. Results Participants found Coping-Together was mostly relevant. All (n = 26, 100%) participants expressed interest and a desire to improve their self-management skills. Perceived benefits included learning to develop SMARTTER (specific, measurable, attainable, relevant, timely, and done together) self-management plans, normalizing challenges, and enhancing communication within the dyad and with their healthcare team. Most (n = 25, 96%) identified strategies from the booklets that benefited them. Top strategies learned were skills to manage physical health (n = 20, 77%) (e.g., monitoring symptoms), emotional well-being (n = 21, 81%) (e.g., reducing stress by reframing thoughts), as well as social well-being (n = 24, 92%) (e.g., communicating with their healthcare team). Barriers included illness severity and time constraints. The unique advanced cancer needs that are to be integrated include support related to fear of death, uncertainty, palliative care and advanced care planning. Suggested modifications involved enhancing accessibility and including more advanced cancer information (e.g., end-of-life planning, comfort care, resources). Significance of results Participants reported several benefits from using Coping-Together, with minimal adaptations needed. Creating SMARTTER self-management plans helped them implement self-management strategies. Specific areas for improvement addressed the need for improved accessibility and more content related to advanced cancer. Findings demonstrate how Coping-Together is acceptable for those living with advanced cancer and their caregivers, offering much of the support needed to enhance day-to-day quality of life.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".