To share or not to share: communication of caregiver-reported outcomes when a patient has colorectal cancer
Bibliographic record
Abstract
BACKGROUND: The importance of patient-centered measurement in cancer care has led to recognition of the potential for caregiver-reported outcomes to improve caregiver, patient and healthcare system outcomes. Yet, there is limited evidence to inform caregiver-reported outcome implementation. Our purpose was to generate evidence to inform the meaningful and constructive integration of caregiver-reported outcomes into cancer care to benefit caregivers, including exploration of the question of the extent to which these assessments should be shared with patients. We focused on caregivers of patients with colorectal cancer (CRC) because CRC is common, and associated caregiving can be complex. RESULTS: From our Interpretive Description analysis of qualitative interview data from 78 participants (25 caregivers, 37 patients, and 16 healthcare providers [HCPs]), we identified contrasting perspectives about the sharing of caregiver-reported outcome assessments with patients with CRC. Those who preferred open communication with both the patient and caregiver present considered this essential for supporting the caregiver. The participants who preferred private communication without the patient, cited concern about caregiver- and patient-burden and guilt. Recognizing these perspectives, HCPs described strategies used to navigate sensitivities inherent in preferences for open versus private communication. CONCLUSIONS: The integration of caregiver-reported outcomes into cancer care will require careful consideration of caregiver and patient preferences regarding the communication of caregiver assessments to prevent additional burden.
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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 | 0.030 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".