Hope and Probability: Patient Perspectives of the Meaning of Numerical Information in Cancer Communication
Bibliographic record
Abstract
Although the complexities inherent in human communication make it a difficult target for empirical investigation and systematic interpretation, it is well recognized that patient-provider communication can have either a powerfully negative or positive influence on the experience of cancer. Drawing on an extensive data set derived from interviews with 200 cancer patients, the authors examine the impact of information provided in numerical form within cancer care communications from the patient perspective. In this context, they present findings related to various uses and abuses of numbers within cancer care communication, and illustrate how numerical information constitutes a specialized communication form with considerable potency for shaping the cancer experience. In particular, accounts of the thematic relationship between numbers and hope, from the perspective of those on the receiving end of cancer care, provide a unique perspective from which to interpret issues of compassion, caring, and informed consent.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".