Bibliographic record
Abstract
BACKGROUND: Recent research shows that cancer survivors are at greater risk of developing cancer than the general population. Although recommended, many cancer survivors receive no regular cancer screening. Cancer survivors' perceptions of their second cancer risk are, in part, suspected to influence their participation in cancer screening. OBJECTIVE: This study was conducted to explore how cancer survivors define and interpret second cancer risk. METHODS: An interpretive descriptive approach was taken whereby semistructured interviews were conducted with 22 cancer survivors (16 women and 6 men) drawn from a provincial cancer registry. The sample ranged in age from 19 to 87 years. The cancer history of the participants varied. Data were analyzed using the constant comparative method of data analysis. RESULTS: The overall theme, "life after cancer-living with risk," described cancer survivors' sense that risk is now a part of their everyday lives. Two themes emerged from the data that speak to how cancer survivors lived with second cancer risk: (1) thinking about second risk and (2) living with risk: a family affair. CONCLUSIONS: Effective risk communication to support the decisions made by cancer survivors with respect to cancer screening is warranted. IMPLICATIONS FOR PRACTICE: Study results provide foundational knowledge about the nature of second cancer risk that may be used to develop and refine standards for survivorship care including how second cancer risk can be best managed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".