The association among cancer patients’ collaboration with their healthcare providers, self‐management and well‐being during radiotherapy: An observational, cross‐sectional survey
Bibliographic record
Abstract
OBJECTIVES: Patients adapt to cancer through self-management, which requires collaboration between patients and their healthcare providers. We aimed to describe patterns of patient-provider collaboration during radiotherapy and examine associations among patient-provider collaboration, self-management and well-being. METHODS: An observational, cross-sectional study was conducted at a cancer centre in the province of Ontario, Canada. Cancer patients (N = 130) completed a one-time questionnaire during their radiotherapy. The questionnaire assessed three variables: collaboration with healthcare providers, self-management and well-being. Patterns of collaboration were analysed using descriptive statistics. Associations among study variables were assessed through structural equation modelling (SEM). Separate models were tested for patient-nurse and patient-oncologist collaboration. RESULTS: Participants reported greater collaboration with oncologists than with nurses or radiation therapists. Most participants reported no collaboration with other providers within healthcare teams (e.g. social workers, dietitians). SEM revealed different patterns for the patient-nurse and patient-oncologist collaboration models, where collaboration predicted one self-management aspect, and both physical and mental well-being. CONCLUSION: During radiotherapy, patients collaborated mainly with doctors, nurses and radiation therapists. Collaborative relationships between patients and providers may enhance patient outcomes by fostering their self-management skills. Initiatives to strengthen patient-provider relationships and support self-management should be developed and applied to interprofessional-cancer-care teams. IMPACT: This is the first known study to empirically support the links among patient-provider collaboration, self-management and patient outcomes. The study results can enhance practice, research and education.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".