Bone fragility related to breast cancer treatment: The pivotal role of nurses in bone health program development, implementation, and testing
Bibliographic record
Abstract
Breast cancer treatment can affect estrogen levels leading to significant bone loss, osteoporosis, and risks for fracture. Although bone care guidelines are published, bone health interventions are often not routinely offered to at-risk individuals. This paper reports on the process of developing and implementing a nurse-led bilingual Breast and Bone Health Program (BBHP) in-person and online at a cancer centre in Montreal, Quebec (www.breastandbonehealth.ca, www.santeseinsetos.ca). The BBHP offers tailored bone health interventions (e.g., risk screening, information, rehabilitation, exercise prescriptions, nutritional counselling, and support for a health-promoting lifestyle). Over a two-year period, women treated for breast cancer (N = 430) took part in the program. Forty percent of surveyed participants (n = 97) initally reported being unaware that some breast cancer treatment could significantly affect bone health. Following the initial informational session with the BBHP nurse, self-reported bone health knowledge significantly increased, with 96% reporting sufficient information to manage their bone health. The BBHP offers both online and in-person risk assessment and bone health promotion activities and tools to both health care professionals and women with breast cancer. Herein, we review the background, BBHP development and implementation as well as preliminary program evaluation.
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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 | 0.006 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".