Assessment of the implementation fidelity of the Arctic Char Distribution Project in Nunavik, Quebec
Bibliographic record
Abstract
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 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 | 0.059 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 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 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".