063PS Challenges and Opportunities in Low-Resource Settings: Guideline Development, Adaptation, Implementation and Performance Measurement
Bibliographic record
Abstract
Background Health care and health policy organisations seeking to improve medical care in low-resource settings with significant numbers of uninsured, medically underserved and/or low-income populations face significant challenges in developing, adapting, implementing and monitoring improvement with clinical practice guidelines. Objectives/Goals To provide opportunities for panel members and conference participants to exchange experiences, challenges, lessons learned, and opportunities for collaboration related to guideline development, adaptation, implementation and performance measurement in low-resource settings. Target Group, Suggested Audience Those involved in guideline development, adaptation, implementation and/or performance measurement activities, especially in medically underserved, low-income and low-resource settings. Description of Session and Speaker Topics The session will include brief speaker presentations, followed by interactive, facilitated discussion between panellists and audience members. Dr. Beena will discuss development of guideline-based quality standards to decrease maternal mortality in rural settings in Kerala, India; Dr. Opiyo will discuss guideline adaptation and implementation efforts in Nairobi to improve child and newborn health; Dr. Lang will discuss the G-I-N Emergency Care Community’s efforts to support collaboration across the field of international emergency care, including adaptation of sepsis guidelines for global use in low-resource settings. Sue Huckson will moderate the discussion.
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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 | 0.115 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".