The missing piece: cancer prevention within psycho‐oncology — a commentary
Bibliographic record
Abstract
In this commentary, we review the place of prevention within the field of Psycho-Oncology. The thrust of Psycho-Oncology's clinical and research efforts have historically focused on behavioral and social factors implicated in the cancer patients' experience from detection and diagnosis, to treatment, survivorship and end of life along the cancer trajectory. This conceptualization has raised the standards for research, leading to a better understanding of the patient experience and the delivery of highly effective interventions to improve quality of life. Emerging data on the role of potential prevention behaviors (e.g., diet and exercise, smoking cessation, screening, etc.) suggests that Psycho-Oncology has a significant role to play in understanding and intervening on a population level to reduce cancer incidence. We present and describe an expanded model of research in Psycho-Oncology which incorporates psychosocial variables in prevention research to complement Holland et al.'s (1998, 2010) original model. The implications of this model are discussed in relation to research, clinical work and training within the discipline of Psycho-Oncology. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.017 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.039 | 0.055 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".