Perceived Barriers to Comprehensive Cleft Care Delivery
Bibliographic record
Abstract
INTRODUCTION: We analyzed the perceptions of participants and faculty members in simulation-based comprehensive cleft care workshops regarding comprehensive cleft care delivery in developing countries. METHODS: Data were collected from participants and faculty members in 2 simulation-based comprehensive cleft care workshops organized by Global Smile Foundation. We collected demographic data and surveyed what they believed was the most significant barrier to comprehensive cleft care delivery and the most important intervention to deliver comprehensive cleft care in developing countries. We also compared participant and faculty responses. RESULTS: The total number of participants and faculty members was 313 from 44 countries. The response rate was 57.8%. The majority reported that the most significant barrier facing the delivery of comprehensive cleft care in developing countries was financial (35.0%), followed by the absence of multidisciplinary cleft teams (30.8%). The majority reported that the most important intervention to deliver comprehensive cleft care was creating multidisciplinary cleft teams (32.2%), followed by providing cleft training (22.6%). We found no significant differences in what participants and faculty perceived as the greatest barrier to comprehensive cleft care delivery (P = 0.46), or most important intervention to deliver comprehensive cleft care in developing countries (P = 0.38). CONCLUSIONS: Our study provides an appraisal of barriers facing comprehensive cleft care delivery and interventions required to overcome these barriers in developing countries. Future studies will be critical to validate or refute our findings, as well as determine country-specific roadmaps for delivering comprehensive cleft care to those who need it the most.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".