Identifying research priorities on infections in older adults: proceedings of an interdisciplinary workshop
Bibliographic record
Abstract
BACKGROUND: Infections pose a substantial burden to the health of older adults. In this report, we describe the proceedings of a workshop to formulate and prioritize research questions about infections in older adults using an interdisciplinary approach. METHODS: Researchers from four sectors (basic science, clinical sciences, health services and epidemiology/determinants of health) and representatives from various Canadian local, provincial, and federal stakeholder groups were invited to a two-day workshop. Five multi-disciplinary groups and stakeholders from each of three healthcare settings (long term, acute care and community) discussed research priorities for each of the settings. Five to ten research questions were identified for each setting. RESULTS: The research questions proposed ranged from risk factors and outcomes for different infections to the effect of nutrition on infection and the role of alternative and complementary medicine in treating infections. Health service issues included barriers to immunization, prolongation of hospital length of stay by infection, use of care paths for managing infections, and decision-making in determining the site of care for individuals with infections. Clinical questions included risk factor assessment for infection, the effectiveness of preventative strategies, and technology evaluation. Epidemiologic issues included the challenge of achieving a better understanding of respiratory infections in the community and determining the prevalence of colonization with multi-resistant bacteria. CONCLUSIONS: The questions are of direct relevance to researchers in a wide variety of fields. Bringing together a multi-disciplinary group of researchers to frame and prioritize research questions about aging is feasible, participants valued the opinions of people working in other areas.
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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 | 0.120 | 0.099 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.016 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".