Bridging the gap: the need to integrate psychosocial oncology services into cancer genetics
Bibliographic record
Abstract
Abstract Cancer susceptibility genes were first cloned over 25 years ago, prompting the initiation of cancer genetics services. Individuals with a strong family history suggesting inherited cancer susceptibility were referred for pretest genetic counseling, with specialist services typically based in academic centers. However, genetic information is now being used to inform personalized medicine approaches to oncology care, ranging from surgical decision making to selection of therapeutic agents for precision treatment. Receiving genetic information is life altering, with relevance for mortality and health practices. The psychosocial impacts of genetic information on individuals and their family have been well documented. Adverse psychological reactions are less common within an applied framework, including clear information and emotional support. Genetics services often occur separate from oncology teams and would benefit from further integration with psychosocial care. Psycho-oncology team members are primed to bring the relevant expertise. Recommendations are offered to help bridge the current gap in psychosocial care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".