A Tale of Two Conditions: 50 Years of Chronic Illness and Cancer From a Nursing Perspective
Bibliographic record
Abstract
INTRODUCTION: In this commentary, I consider the disparity between the care we see as a necessity for cancer patients and the lack of care we afford to many persons with chronic disease. BACKGROUND: I ground my arguments in my own clinical practice and research experiences over a 50-year period augmented by reference to available literature sources. DATA SOURCES: In tracing developments within the fields of cancer and chronic illness care, I draw on my own research and that of others. OVERVIEW OF THE ISSUE: Although chronic illness has long been recognised as causing the majority of the burden on our health care systems and as a significant source of suffering in our society, it has not attracted the level of enthusiasm from researchers, policy makers, and health care systems that we have seen in the context of other diseases such as cancer. Nurses have an intimate knowledge of the suffering occasioned by chronic illness; however, it has been difficult for nursing to mobilise coordinated action in prioritising a re-balance of health systems to better serve those with chronic conditions. FINDINGS: The advent of medical assistance in dying in Canada has shed a spotlight on the implications of the discrepancy between our prioritising patient need in the care and support of patients with conditions such as cancer, in contrast with the supports and services we make available to those with chronic conditions. DISCUSSION: Although nursing intimately engages with the burden of chronic illness, it has not mobilized coherent advocacy toward strengthening our societal commitment to this aspect of our care systems. CONCLUSION: There is an opportunity for nursing to make a meaningful difference in a fundamental health care system inequity if we can come to understand that chronic illness is as deserving of our collective research, practice change, and policy attention as is cancer. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.017 | 0.039 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".