Prognosticating futures and the human experience of hope
Bibliographic record
Abstract
OBJECTIVES: Communication between health care providers and patients with cancer and other chronic diseases typically references probabilities that certain future events will or will not occur. Beyond the context of diagnostic encounters and the transmission of "bad news," such "prognostic" communications take place in various forms throughout the illness trajectory. It is well known that such information transmitted badly can have devastating psychosocial consequences for patients and their families and, conversely, that difficult information exchanged with sensitivity can lend tremendous support. This study aimed to extend our understanding of how such communications are received and interpreted by patients, so that we might optimally apply what we know about general principles of effective communication within the particularly challenging context of predicting futures. METHODS: We conducted a combined secondary analysis of two prior qualitative studies into patient perceptions of helpful and unhelpful health care communication with 200 cancer patients and 30 persons with chronic illness. These data sets offered a rich resource for comparing perceptions across a range of contextual variables, and secondary analysis focused on future-oriented interactions, including both prognostication and prediction. RESULTS: The accounts of patients with cancer and chronic illness reveal various ways in which health care communications involving future projections interact with their human experience of hope, powerfully shaping their capacity to make sense of and live with serious illness. They include a synthesis of what patients recommend health care professionals know and understand about this challenging dynamic. SIGNIFICANCE OF RESULTS: The findings of this study offer a distinct angle of vision onto the various communications that involve future predictions, illuminating a patient perspective with the potential to inform health care communication approaches that are both informative and therapeutic. As such, the study supports a dynamic understanding of the tenuous balance between hope and honesty in the clinical encounter.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".