Innovative application of strategic foresight to oncology research
Bibliographic record
Abstract
Purpose This study aims to investigate into the future of cancer and cancer research in preparation for a strategic plan for a cancer research centre. Design/methodology/approach The study used framework foresight, a method for creating scenarios and their implications developed by the MS program in Foresight at the University of Houston. Findings The study identified four scenarios: a continuation scenario in which progress in detecting and treating cancer progressed as it has over the past few decades, a collapse scenario in which attention was diverted from medical research due to a climate crisis, a new equilibrium scenario in which cost became the overriding concern for cancer treatment, and a transformation scenario in which individuals took control of their treatment through Do-It-Yourself remedies. Those scenarios suggested four strategic issues for the planning exercise: the growing volume of genomic and clinical data and the means to learn from it, the increased involvement and influence of patients in diagnosis and treatment, the ability to conduct research in a time of fiscal austerity and declining levels of trust in all professions, including medicine. Research limitations/implications The paper not only provides guidance for cancer centers but also for medical practice in general. Practical implications The client used the scenarios and their implications as part of its considerations in strategic planning. Originality/value This paper represents the first time that Framework Foresight has been applied to a medical topic.
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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 | 0.045 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".