Cancer Care From the Perspectives of Older Women
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To understand how older age affects cancer care, from the perspectives of older women. RESEARCH APPROACH: Qualitative, participatory. SETTING: Urban southern region of Ontario, Canada. PARTICIPANTS: Purposive sample (age groups and income) of 15 women diagnosed with cancer at age 70 or older; 10 women were diagnosed with breast cancer, 5 with gynecologic cancer. METHODOLOGIC APPROACH: Two face-to-face interviews, with data analysis in collaboration with the project team based on constructivist grounded theory, including negative case analysis. MAIN RESEARCH VARIABLES: Age, experience of cancer care. FINDINGS: Age-related life and health circumstances intersect with professional practice and wider social contexts and are implicated in treatment decision making, including decisions against treatment, as well as in the day-to-day "getting around" that cancer care requires. CONCLUSIONS: The nursing history should be holistic in scope, attending to the supportive care domains to elicit older women's physical, social, practical, informational, psychological, and spiritual needs after a diagnosis of cancer. History taking should draw forward older women's life contexts and examine these contexts in relation to cancer care, including treatment decision making. INTERPRETATION: Individual-level care and systems advocacy are required to ensure that older women's worries about sustaining independence, including worries generated by inadequacies in home-based care, do not act as determinants of treatment choices.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".