Bibliographic record
Abstract
Background: Cancer patients can be socially isolated and lonely. The general public still has negative attitudes toward cancer and cancer patients. It is important for cancer patients to have a supportive environment and to connect with other people. Methods : We conducted a qualitative study with two focus group interviews of eight persons using a grounded theory approach to understand ordinary people’s perceptions of cancer and cancer patients, and to explore their experience of interacting with the patients. Results: This study revealed “Communication across cancer boundaries” as a core category with six themes: “negative assumptions,” “social stigma,” “communication boundaries,” “transforming perceptions of cancer patients through interactions,” “building communication competence,” and “awakening empathy.” The ordinary people still had negative assumptions about cancer and cancer patients, leading to social stigma and creating communication boundaries. The experience of interaction with a cancer patient, however, altered their views on cancer patients, and reminded them of the need for communication competence to create a relationship with empathy. Conclusions: The results of this study provided unique insights into ordinary people’s views on cancer and cancer patients. Health care providers should understand how ordinary people perceive cancer patients, and provide education and they should provide information to both cancer patients and the general public. A society’s greater understanding of cancer and cancer patients enables the society to provide an empathetic community where cancer is no longer seen as a taboo and stigmatized.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".