APPROACHES OF CANADIAN PROVIDERS TO THE DIAGNOSIS OF FETAL ALCOHOL SPECTRUM DISORDERS
Bibliographic record
Abstract
Background A better understanding of the attitudes and knowledge of providers towards diagnosis of fetal alcohol spectrum disorders (FASD) will assist in the development of appropriate supports and interventions. Objective To determine approaches of providers to the diagnosis of FASD. Methods Between October 2001and May 2002, a survey was mailed to a national random sample of paediatricians, psychiatrists, obstetricians and gynaecologists, family physicians, and midwives in Canada, who were current members of professional organisations (N=5361). Results The overall response rate was 41.3%. Over 90% of providers agreed that a diagnosis of fetal alcohol syndrome (FAS) can change things for affected children and 75% agreed making a diagnosis is within their scope of practice. The most noted barrier to diagnosis was lack of training (56.4%). The most common sources of FAS information were medical journals (76.4%), medical school (63.6%), and Continuing Medical Education (CME) seminars (50.9%). Approximately 60% of providers correctly identified the combination of growth, brain and facial abnormalities as providing the most accurate diagnosis of FAS. Over 60% of providers identified emotional disorders, disrupted school experience, addictions and legal problems as long term outcomes associated with FAS. There were significant differences (p≤0.001) across provider group with regard to scope of practice, barriers to diagnosis, source of knowledge, diagnostic knowledge, and understanding of long term outcomes.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 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) | 0.010 | 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".