Comprehensive Cleft Care Delivery in Developing Countries: Impact of Geographic and Demographic Factors
Bibliographic record
Abstract
INTRODUCTION: The authors analyzed the insights of participants and faculty members of Global Smile Foundation's Comprehensive Cleft Care Workshops concerning the barriers and interventions to multidisciplinary cleft care delivery, after stratification based on demographic and geographic factors. METHODS: During 2 simulation-based Comprehensive Cleft Care Workshops organized by Global Smile Foundation, participants and faculty members filled a survey. Surveys included demographic and geographic data and investigated the most relevant barrier to multidisciplinary cleft care and the most significant intervention to deliver comprehensive cleft care in outreach settings, as perceived by participants. RESULTS: The total response rate was 57.8%. Respondents reported that the greatest barrier to comprehensive cleft care was financial, and the most relevant intervention to deliver multidisciplinary cleft care was building multidisciplinary teams. Stratification by age, gender, and geographical area showed no statistical difference in reporting that the greatest barrier to cleft care was financial. However, lack of multidisciplinary teams was the most important barrier according to respondents with less than 5 years of experience (P = 0.03). Stratification by gender, years in practice, specialty, and geographical area showed no statistical difference, with building multidisciplinary teams reported as the most significant intervention. However, increased training was reported as the main intervention to cleft care for those aged less than 30 years old (P = 0.04). CONCLUSIONS: Our study delivers an assessment for barriers facing multidisciplinary cleft care delivery and interventions required to improve cleft care delivery. The authors are hoping that stratification by demographic and geographic factors will help them delineate community-specific road maps to refine cleft care delivery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".