Feasibility and acceptability of a brief intervention promoting self-care behaviours among cancer caregivers
Bibliographic record
Abstract
Background: To help caregivers manage the unique demands of providing care for someone who has been diagnosed with cancer, we developed a brief self-determination theory-based eHealth intervention to increase caregivers' self-care behaviours (operationalized as physical activity and fruit and vegetable consumption). Using a mixed-methods approach, the main objective of this study was to assess the feasibility and acceptability of our intervention. Methods: Our intervention included 4 weekly one-on-one autonomy supportive interactive video-calls with a health and wellness advisor. Quantitative data were collected pre- and post-intervention and analyzed descriptively. Semi-structured interviews were conducted post-intervention and analyzed using thematic analysis. Results: From January to June 2019, 7 caregivers (Mage=63.9, SD=12.2, 71.4% female) were recruited via community organizations, social media, and word of mouth. Recruitment/enrollment (53.8%) was low; however, adherence (100%), fidelity (99%), and retention (100%) rates were high after enrollment. Participants generally expressed satisfaction with the intervention content, delivery mode, frequency, personalized approach, and support received from the advisor, though some desired more sessions over a longer period of time. Three themes captured caregivers' experiences within the intervention: (1) building and maintaining supportive relationships; (2) refocusing on self-care; and (3) engaging in self-care to be a better caregiver. Conclusions: Individually tailored interventions may empower cancer caregivers to prioritize self-care and focus on their own social, emotional, and physical health, and in turn enhance their ability to provide care. Co-designing recruitment strategies with caregivers and partnering with organizations who provide services to cancer survivors and caregivers may facilitate recruitment for future interventions.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".