Bibliographic record
Abstract
Globally, the number of older people with multiple chronic conditions (MCCs) has increased dramatically in the past decades. It is not unusual for an 80-year old to be diagnosed with five or six conditions, each treated with multiple medications and complex therapeutic regimens. While some countries have developed innovative policies and programs designed to improve the lives of people with MCCs, other countries are just beginning to respond to these challenges. Typically, the onus is on individuals and families to manage the demands of MCCs. Yet knowledge about how people manage MCCs, what supports enable people with MCCs to cope effectively, and what role medical practitioners should play is unclear. This symposium includes presentations from scholars in Bulgaria (Stanimir Hasardzhiev), Canada (Walter Wodchis), Italy (Alessandra Marengoni), Lithuania (Rokas Navickas), and the United States (Maureen Wilson-Genderson & Allison Heid). Presenters will discuss the number of older people with MCCs, how older people cope with MCCs, the association between onset of MCCs and outcomes, the effects of social support on MCCs, the importance of person-centered care in the context of MCCs, the economic costs of MCCs, and responses of global health care systems in addressing the needs of people with MCCs. Discussant Cynthia Boyd will highlight the similarities and differences in the work of these international scholars, examine how findings can build on one another, and suggest how research can be used to change the way care is provided to older people, thereby improving the lives of older people with MCCs worldwide.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".