Interaction between professionals and cancer survivors in the context of Brazilian and Canadian care
Bibliographic record
Abstract
ABSTRACT Objective: analyze cancer survivors’ reports about their communication with health professional team members and describe the similarities and differences in interactional patterns between Brazilian and Canadian health care contexts. Method: This study adopted a qualitative health research approach to secondary analysis, using interpretive description as the methodology, allowing us to elaborate a new research question and look at the primary data from a different perspective. There were in total eighteen participants; all of them were adults and elderly diagnosed with urologic cancer. After being organized and read, the data sets were classified into categories, and an analytic process was performed through inductive thematic analysis. Results: This resulted in three categories of findings which we have framed as: Communication between professional and survivor; The symptoms, the doubts, the questions; and Actions and reaction. Conclusion: This comparative study allowed us to bring to the attention of health professionals, especially nurses, findings regarding effective communication, humanization and empathy, supporting both inside and outside support groups, giving pieces of advice, and advocating for the survivor as is necessary. The study also showed the importance of self-development of these professionals as they fight for better quality in the health system for their patients.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".