Equality in cleft and craniofacial care
Bibliographic record
Abstract
This review examines the issue of equality of care amongst those with cleft lip and/or palate in the European Union (EU) and beyond. Issues of equality both between and within national populations are considered, and it is argued that those from countries with smaller healthcare expenditure and who are from marginalised groups are at the greatest risk of, and affected most severely by, healthcare inequalities. The socioeconomic impact of inequality is also discussed. Having reviewed these topics, the goals and activities of the European Cleft and Craniofacial Initiative for Equality in Care Action, formed pursuant to an award from the EU’s European Cooperation in Science and Technology, are introduced. Constituted of an open network of clinicians and researchers committed to exploring and reducing such inequalities, the ongoing Action is formed of multiple working groups examining these issues within the EU and has organised training schools, conferences and short-term scientific missions concerned with these issues. These activities are discussed along with the future directions of the Action, the impact it has had to date and the benefits of the European Cooperation in Science and Technology award.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".