The INGUIDE International Guideline Training and Certification Programme
Bibliographic record
Abstract
Abstract Health guidelines impact clinical, public health and policy practice, but there is no regulation for their development, often leading to variability in quality and trustworthiness. The International Guideline Training and Certification Programme (INGUIDE), established by faculty at McMaster University in partnership and under the auspice of the Guidelines International Network (GIN), addresses this shortcoming by offering structured, evidence‐based training and certification for those involved in guidelines ( inguide.org ). This commentary describes INGUIDE's background, purpose, structure and significance after approximately 3 years of operation. INGUIDE's mission is to enhance the quality of health guidelines globally. It provides comprehensive training to ensure the systematic development of guidelines based on the best evidence and adherence to international quality standards, as reflected in its ISO:9001 certification. The programme also emphasizes capacity building, filling educational gaps, and ensuring the global inclusivity of its courses. INGUIDE's certification covers the entire lifecycle of guideline development and includes several levels of certification, ranging from panel member to methodologist, lead methodologist, developer, chair and instructor certification. The programme already has had a global impact, training over 1500 learners since its launch. INGUIDE is led by a steering committee with input from an international advisory board and operational staff, supported by certified instructors. The programme's vision for the future includes expanding accessibility and creating additional training modules, with a commitment to continuous improvement and adaptation to diverse healthcare contexts, in particular low‐ and middle‐income countries and settings.
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".