Nurturing and Optimizing Networks of Care to Maximize Benefits to Patients, Health Workers, and Health Systems
Bibliographic record
Abstract
I n a commentary summarizing a landscape review of Networks of Care conducted by the World Health Organization, Kalaris et al. 1 argue that Networks of Care can improve quality and continuity of care and strengthen health systems functions by improving relationships between different providers-and that all these improvements will accelerate progress toward the Sustainable Development Goals.This is a lot of hope to place on one approach.Often in global health, promising practices such as these are oversimplified and oversold as a panacea, ultimately underdelivering on their promise of success.Although we share the authors' optimism regarding Networks of Care, we think that to ensure that Networks of Care ultimately lead to more effective, replicable patient care, global health practitioners must galvanize more implementation research, operations management capacities, and facilitative policies that elucidate intrinsic and extrinsic factors influencing and unlocking the potential of Networks of Care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.057 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 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".