Challenges and strengths experienced by fetal alcohol spectrum disorder diagnostic clinics in Canada
Bibliographic record
Abstract
BACKGROUND: The Canadian fetal alcohol spectrum disorder (FASD) diagnostic guideline provides clinicians with the process and procedure to reach an accurate diagnosis. However, organisational structure, culture, and resource utilisation vary. The objectives of this study were to identify the key challenges and strengths of successful FASD diagnostic clinics. METHOD: Qualitative interviews were conducted with 12 key informants from 10 clinics representing different regions, populations served, and clinic structures. Data analysis was performed using iterative thematic inquiry. RESULTS: Three themes related to challenges and four themes related to strengths were identified. Human resources were identified as both a challenge and strength. Additional challenges were diagnostic capacity and system level support. Additional strengths were clinic adaptability, relational connections, and culturally responsive approaches. CONCLUSIONS: FASD clinics are more alike than not in their approach to assessment and diagnosis. Some clinics are facing similar challenges that others have overcome, supporting the need for mentorship and consistent operating standards.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".