The Relationship between Therapeutic Alliance and Quality of Care in Patients with Advanced Cancer in Spain
Bibliographic record
Abstract
The therapeutic alliance is an important factor in successful cancer treatment, particularly for those with advanced cancer. This study aims to determine how the therapeutic alliance relates to prognostic preferences and satisfaction with the physician and medical care among patients with advanced cancer. We conducted a cross-sectional study to explore the therapeutic relationship, trust, satisfaction with healthcare, and prognostic preferences among 946 patients with advanced cancer at 15 tertiary hospitals in Spain. Participants completed questionnaires with self-reported measures. Most were male, aged > 65 years, with bronchopulmonary (29%) or colorectal (16%) tumors and metastatic disease at diagnosis. Results revealed that 84% of patients had a good therapeutic alliance. Collaborative and affective bond was positively associated with a preference to know the prognosis and satisfaction with care and decision. There was no difference in a therapeutic alliance based on clinical or sociodemographic factors. The therapeutic alliance between patient and physician is essential for successful treatment outcomes and better overall satisfaction. Therefore, it is vital for healthcare providers to focus on establishing and maintaining a strong relationship with their patients. To achieve this, transparency and care should be prioritized, as well as respect for the preferences of patients regarding the prognosis of their illness.
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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 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".