Post-traumatic growth following breast cancer treatment
Bibliographic record
Abstract
Worldwide, breast cancer is the most commonly diagnosed cancer amongst women. Currently, in Canada, it is estimated that approximately 1 in 9 women will develop breast cancer at some point in their life, and 1 in 30 will die from it. With the growing proportion of women diagnosed with breast cancer each year, there is an expanding body of literature addressing the effect of undergoing breast cancer treatment. The survival rate has increased since the mid-1980s, and as such, more women are in remission from their treatment. To coincide with this, breast cancer literature has begun addressing the effects experienced by women after their treatment. The purpose of this article is to briefly review literature that suggests that women may experience post-traumatic growth after completing breast cancer treatment. Post-traumatic growth, or experiencing positive outcomes as a result of undergoing an extremely negative or traumatic experience, can include developing a new perspective on life, adopting routines to enhance one’s quality of life (such as diet and exercise habits), as well as increasing spirituality. This article will outline some potential outcomes of post-traumatic growth, as well as provide insight to health care providers on how to aid in transitioning women with breast cancer from treatment to life in remission.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".