8.K. Workshop: Shared impact: How to foster innovation in public health data
Bibliographic record
Abstract
Abstract In 2018, Canadian Partnership for Women and Children's Health (CanWaCH), a network of Canadian civil society, professional, and academic actors working collectively on global health and gender equality, launched 'The Canadian Collaborative in Global Health'. This initiative was designed specifically to build the capacity of Canadian actors, and their global partners, to better collect, analyze, synthesize and report data and evaluation activities through new, innovative approaches, in order to strengthen their impact in the areas of SDGs 3 and 5. Through this interactive workshop, participants will be able to (a) learn about the 3 diverse innovations that are being incubated under the Collaborative model through diverse multi-sectoral partnerships; (b) dive deeply into 3 innovative practices focusing on nutrition, sexual and reproductive health and rights, and adolescent health; and (c) explore how they might replicate this approach, and discuss critical issues relating to the fostering innovative thinking and approaches when it comes to data collection in public health. Representatives from civil society organisations, academic institutions, and private sector will outline strategies for how interdisciplinary collaborations can be effectively implemented and executed. Key messages CanWaCH’s innovative participatory methodology has generated significant new findings to guide accountability, programming, policy and advocacy. and can/should be replicated elsewhere. Three specific global health data innovations will be presented, featuring brand new tools, indicators, and strategies which can help participants with their work.
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 | 0.224 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.007 | 0.039 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".