Seven lessons from the field: Research on transformation of health systems for older adults
Bibliographic record
Abstract
Research can play a key role in efforts to transform healthcare systems. Our group's long-standing research program has been aimed at understanding how to support greater integration and coordination of healthcare services for older adults with complex conditions. Drawing on this experience, we outline seven "lessons from the field" that highlight research-related challenges that may hinder health system transformation. These challenges relate to conducting research in a complex and constantly changing system; co-design approaches that are simultaneously deemed essential yet too ambiguous to fund; patient, family caregiver, and citizen engagement; limited funding for health systems research; and lack of use of research findings. We hope that these reflections will help to inform an ongoing conversation about how these challenges might be overcome.
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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 | 0.143 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.025 | 0.051 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 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".