The Impact of Social Stigma for Children with Cleft Lip and/or Palate in Low-resource Areas: A Systematic Review
Bibliographic record
Abstract
There are still children with cleft lip and/or palate (CLP) in low-resource areas who face social rejection. This stigma disadvantages children in education, employment, marriage, and community, and is exacerbated by barriers to care. Our study objective was to conduct a systematic review of the impact of social stigma of CLP for children in low-resource areas. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic search was conducted of 3 databases: Ovid Embase, Ovid Medline, and the African Journal Online from 2000 to October 5 2018. Common themes were identified using a grounded theory approach and quantitatively summarized. The Joanna Briggs Institute criteria were used to evaluate the risk-of-bias assessments. Four hundred seventy-seven articles were screened; 15 articles were included that focused on the impact of social stigma on CLP in low-resource areas. This was limited to English articles. The majority of studies originated in Nigeria or India. Themes were reported as follows: societal beliefs (n = 9; 60%), social impact (n = 7; 46%), marriage (n = 7; 46%), education (n = 6; 40%), employment (n = 5; 33%), and psychological distress (n = 3; 20%). Causes include the effect of "God's will," supernatural forces, evil spirits or ancestral spirits, exposure to an eclipse, black magic, or a contagion. Further, children with CLP may not be worth a full name or considered human and killed. Awareness of the impact of social stigma for children with CLP in low-resource areas generates support toward national education and awareness in low-resource areas.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".