Addressing FASD in British Columbia, Canada: analysis of funding proposals.
Bibliographic record
Abstract
BACKGROUND: Fetal Alcohol Spectrum Disorder is a preventable health issue affecting about 10% of the population. This research examined proposals submitted to a call for funding for projects to improve outcomes for people with fetal alcohol spectrum disorder (FASD). OBJECTIVES: The aim was to use the proposals as proxy for perceptions of needs held by practitioners in British Columbia, Canada, where considerable FASD-related education and awareness exists. METHODS: Content analyses were conducted and Chi-square tests were used to test the relationship between proposal foci, community size and the submitting agency's experience with FASD. RESULTS: Nine foci were found: Skill Development, Care, Training, Resource Development, Education, Transition, Peer Support, Research and Other. No statistically significant difference was found in proposal foci according to size of community, and only one focus, Research, was associated with agency experience. Proposals varied in intensity, timing, participants, and focus of change (people or environments). CONCLUSIONS: Analysis of the proposals provides a unique view into perceptions regarding ways to improve outcomes for people with FASD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".