Guest Editorial: Chronic Illness in the Next Millennium: Context and Complexity
Bibliographic record
Abstract
This is the second occasion on which the Canadian Journal of Nursing Research has devoted an issue to the theme of chronicity. The first, in 1996, marked a time when nursing research was shifting from a generation of medical, illness, and disease models to a more psychosocially oriented understanding of persons who happen to be living with a chronic condition (Burke, 1996). The central themes for that issue were trajectory and transferability, both calls to understand chronicity as a dynamic process and to recognize chronic illness as a generic specialty focus within the discipline. Only 4 years later, those shifts seem almost ancient history and the rapid proliferation of a more person-centred, contextualized approach to our inquiries seems self-evident (Thorne & Paterson, 1998). Certainly, we still need studies of discrete relationships, effects, and outcomes, but we now understand them to be one aspect of a larger picture and not the main focus of our nursing science. Our field is indeed maturing, much as the editor of that first chronicity issue predicted it would. Over the intervening years, the idea of studying chronic illness as an interesting and relevant concept in its own right has been widely popularized and a strong body of nursing scholarship in the field has emerged (Thorne & Paterson, 2000). While specific diseases still warrant direct inquiry, we no longer section ourselves off into disease-oriented multidisciplinary substantive groupings and we have learned how to speak the language of our theories and propositions to each other in a more collective voice. In addition, we have begun to support our consumer advocacy partners in a similar effort, to recognize that chronic illnesses, whatever their manifestations, share a common focus that has relevance for health-care planning, service delivery, and social policy (Jamison, 1998). As nurses and as researchers, we are beginning to adopt social-action strategies and to build alliances with disease-oriented
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.017 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".