Occupational and Financial Setbacks in Caregivers of People with Colorectal Cancer: Considerations for Caregiver-Reported Outcomes
Bibliographic record
Abstract
Family caregivers of patients with cancer provide substantial physical, emotional, and functional care throughout the cancer trajectory. While caregiving can create employment and financial challenges, there is insufficient evidence to inform the development of caregiver-reported outcomes (CROs) that assess these experiences. The study purpose was to describe the occupational and financial consequences that were important to family caregivers of a patient with colorectal cancer (CRC) in the context of public health care, which represent potential considerations for CROs. In this qualitative Interpretive Description study, we analyzed interview data from 78 participants (25 caregivers, 37 patients, and 16 healthcare providers). Our findings point to temporary and long-term occupational and financial setbacks in the context of CRC. Caregiving for a person with CRC involved managing occupational implications, including (1) revamping employment arrangements, and (2) juggling work, family, and household demands. Caregiver financial struggles included (1) responding to financial demands at various stages of life, and (2) facing the spectre of lifelong expenses. Study findings offer novel insight into the cancer-related occupational and financial challenges facing caregivers, despite government-funded universal health care. Further research is warranted to develop CRO measures that assess the multifaceted nature of these challenges.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".