Thinking Forward: Future-oriented Thinking among Patients with Tobacco-associated Thoracic Diseases and Their Surrogates
Bibliographic record
Abstract
RATIONALE: The goal of shared decision making is to match patient preferences, including evaluation of potential future outcomes, with available management options. Yet, it is unknown how patients with smoking-related thoracic diseases or their surrogates display future-oriented thinking. OBJECTIVES: To document prevalent themes in patients' and potential surrogate decision makers' future-oriented thinking when facing preference-sensitive choices. METHODS: We conducted 44 scenario-based semistructured interviews among a diverse group of outpatients with smoking-associated thoracic diseases and potential surrogates for whom one of three preference-sensitive decisions would be medically relevant. Using content analysis, we documented prevalent themes to understand how these individuals display future-oriented thinking. MEASUREMENTS AND MAIN RESULTS: Patients and potential surrogates generally expressed expectations for future outcomes but also acknowledged their limitations in doing so. When thinking about potential outcomes, decision makers relied on past experiences, including those only loosely related; perceived familiarity with treatment options; and spirituality. The content of these expectations included effects on family, emotional predictions, and prognostication. For surrogates, a tension existed between hope-based and fact-based expectations. CONCLUSIONS: Patients and surrogates may struggle to generate expectations, and these future-oriented thoughts may be based on loosely related past experiences or unrealistic optimism. These tendencies may lead to errors, preventing selection of treatments that promote true preferences. Clinicians should explore how decision makers engage in future-oriented thinking and what their expectations are as a component of the shared decision-making process. Future research should evaluate whether targeted guidance in future-oriented thinking may improve outcomes important to 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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".