Letter to the Editor: Why do pregnant South African women drink alcohol? A call to action for more qualitative investigations
Bibliographic record
Abstract
Olusanya, O., & Barry, A. (2015). Letter to the Editor: Why do pregnant South African women drink alcohol? A call to action for more qualitative investigations. The International Journal Of Alcohol And Drug Research, 4(2), 171-174. doi:http://dx.doi.org/10.7895/ijadr.v4i2.213Even though the adverse effects of alcohol consumption during pregnancy have been well documented, millions of babies each year continue to be affected by fetal alcohol spectrum disorders (FASD). This is concerning given that FASD is completely preventable. FASDs have been documented across a variety of races and geographical regions worldwide, yet the highest known prevalence rates are recorded in Africa. Specifically, for every 1000 children born in the Western Cape Province of South Africa, approximately 59.3 to 91.0 are determined to have fetal alcohol syndrome, the most severe form of FASD. While the risk factors contributing to FASDs have been examined quantitatively among South African women, there is a dearth of qualitative investigations that articulate and contextualize the underling motivations, beliefs, and attitudes that influence these risk factors. Qualitative investigations have been conducted in other geographic regions (e.g., Australia), but are not generalizable to South Africa. Qualitative investigations, which explore the familial, social, cultural, and economic factors that influence maternal drinking, are needed to inform future health promotion programs and interventions aimed at decreasing and ultimately eliminating maternal alcohol consumption among South African women.
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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 | 0.017 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".