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Evaluation of a Successful Fetal Alcohol Spectrum Disorder Coalition in Ontario, Canada

2010· article· en· W2030586731 on OpenAlexaffabout
Donna M. Clarke-McMullen

Bibliographic record

VenuePublic Health Nursing · 2010
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsFetal Alcohol Spectrum DisorderHierarchyPromotion (chess)Action (physics)Task (project management)Fetal alcoholPublic relationsPublic healthOutcome (game theory)Political scienceBusinessMedicinePsychologyNursingPregnancyEconomicsManagement

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.136
GPT teacher head0.444
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2010
Admission routes2
Has abstractyes

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