Evaluation of a Successful Fetal Alcohol Spectrum Disorder Coalition in Ontario, Canada
Bibliographic record
Abstract
Leading a successful coalition that benefits both the members and the community is a difficult task. Coalitions are complex and require a great deal of skill to initiate, lead, and evaluate. This article examines a successful coalition, developed to build community capacity to address fetal alcohol spectrum disorder (FASD). FASD is a complex, multidimensional health issue common in many communities. Coalitions can be effective in tackling these types of issues and fit with community capacity-building approaches to health promotion. The Internal Coalition Outcome Hierarchy (ICOH) model (Cramer, Atwood, & Stoner, 2006a, 2006b) is used to retrospectively examine the internal constructs of the FASD Action Network and provide useful lessons learned for other coalition leaders and public health nurses. This hierarchical model demonstrates that sound internal processes lead to more successful outcomes and ultimately an increased impact on community issues. The usefulness of ICOH as a tool in evaluating the FASD Action Network and its application to other health-promotion situations with community capacity goals is described in this article.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".