The salience of existential concerns across the cancer control continuum
Bibliographic record
Abstract
Despite significant improvement in cancer survival, the fear of death still remains rooted in individuals' beliefs about cancer. Existential fears pertaining to cancer cut across the cancer control continuum and taint decisions related to prevention, screening, surveillance, and follow-up recommendations, as well as the overall management of cancer-related issues. However, individuals are innately predisposed to cope with their cancer-related fears through mechanisms such as reliance on the process of meaning making. To better appreciate the potential impact of existential concerns across the cancer control continuum, the Temporal Existential Awareness and Meaning Making (TEAMM) model is proposed. This tripartite model depicts three types of perceived threats to life related to cancer including a "social awareness" (i.e., cancer signals death), "personalized awareness" (i.e., I could die from cancer), and the "lived experience" (i.e., It feels like I am dying from cancer). This construal aims to enhance our understanding of the personal and contextual resources that can be mobilized to manage existential concerns and optimize cancer control efforts. As such, existential discussions should be considered in any cancer-related supportive approach whether preventive, curative, or palliative, and not be deferred only until the advanced stages of cancer or at end of life. Further delineation and validation of the model is needed to explicitly recognize and depict how different levels of existential awareness might unfold as individuals grapple with a potential, actual, or recurrent cancer.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| 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".