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Record W2532569959 · doi:10.1136/bmjgh-2016-000093

Assessment of the implementation fidelity of the Arctic Char Distribution Project in Nunavik, Quebec

2016· article· en· W2532569959 on OpenAlexafffundabout
Lara Gautier, Catherine M. Pirkle, Christopher Furgal, Michel Lucas

Bibliographic record

VenueBMJ Global Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversité LavalThe Quebec Population Health Research NetworkTrent UniversityUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsArctic charDistribution (mathematics)Ethnic groupFidelityWork (physics)Environmental healthQualitative propertyDocumentationEnvironmental planningGeographyEnvironmental resource managementPolitical scienceMedicineFisheryFish <Actinopterygii>EngineeringEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Background In September 2011, the Nunavik Regional Board of Health and Social Services began supporting the Arctic Char Distribution Project (AC/DP) for pregnant women. This initiative promoted consumption of the fish Arctic char—a traditional Inuit food—by pregnant women living in villages of Nunavik, an area in northern Quebec (Canada) inhabited predominantly by people of Inuit ethnicity. This intervention was intended to reduce exposure to contaminants and improve food security in Inuit communities. Methods We assessed the project's implementation based on data collected from background documentation, field notes and qualitative interviews with project recipients and implementers. Themes emerging from the data are critically discussed in the light of the framework for implementation fidelity developed by Carroll et al in 2007. Results Pregnant women fully embraced the initiative because of its cultural appropriateness. However, project implementation was incomplete: first because it did not cover all intended geographic areas, and second because of a recurring inconsistency in the supply and distribution of the fish. In addition, the initiative has been inconsistently funded and relies on multiple funding sources. Discussion This work highlights the extent to which project complexity can impede successful implementation, particularly in terms of communication and coordination. We provide recommendations for improving project implementation and suggest amendments to the implementation fidelity framework.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.511
Teacher spread0.464 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2016
Admission routes3
Has abstractyes

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