The role of general practitioners in Reunion in detecting alcohol use in pregnant women and identifying fetal alcohol spectrum disorder: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Fetal Alcohol Spectrum Disorder (FASD) is the leading cause of non-genetic intellectual disability and social maladjustment in children. International guidelines recommend abstinence from alcohol during pregnancy. Réunion is the most affected of all French regions with an estimated Fetal Alcohol Spectrum (FAS) prevalence of 1.2‰ births. General practitioners (GPs) are at the forefront of identifying patients with FASD. OBJECTIVE: To understand how GPs identify FASD. METHODS: Qualitative study using a grounded theory approach, through semi-structured face-to-face interviews with GPs. Interviews were conducted with the aim of reaching theoretical saturation. These were transcribed verbatim and then analyzed by four researchers to ensure triangulation of the data. RESULTS: GPs reported barriers to the identification of FASD: challenges in overcoming social taboos and paradoxical injunctions, the influence of limited knowledge and experience, non-specific and highly variable symptoms, ambiguous classification and method of diagnosis involving the mobilization of a multidisciplinary team and lengthy consultations. Conversely, they felt competent to identify neurodevelopmental disorders of any cause, but were concerned about the long waiting time to access specialized care. From the perspective of GPs, it is crucial to prioritize promotion and training aimed at improving the identification and coordination of care pathways for children diagnosed with neurodevelopmental disorders, such as FASD.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".