Community Ecosystem Mapping in the Nigerian Calgarian Community: An Exploration for Meaningful Community Engagement.
Bibliographic record
Abstract
Background: Current research highlights a limited knowledge of the barriers Nigerian immigrants face accessing primary healthcare in Canada. Understanding their circumstances can inform public policy that alleviates these burdens and improve their overall health outcomes. Doing so requires understanding the community, its history, cultural spaces, and traditions that encompasses Nigerian immigrants. This project follows a community-based participatory approach that gains insight into the Nigerian community. Method: Following a model of community engagement adapted by Chowdhury et al. (2022) with the Bangladeshi community, I mapped the Nigerian community ecosystem in Calgary, Alberta. This included gathering my observations as a Nigerian community member, utilizing census and archival data, and meeting with community champions. I also reviewed previous literature involving Nigerian immigrants globally. This multi-faceted community-engaged approach helped me capture the social dynamics and integration patterns. Results: 24.1% of all Africans in Calgary are Nigerian. Most Nigerians tend to come from the Southern portion of Nigeria, comprised mostly of the Igbo and Yoruba tribes, due to wealthy oil and gas industries, whereas the North consists of the agricultural Hausa tribe. Local socio-cultural organizations, like the Nigerian Canadian Association of Calgary, restaurants like Delish Dining, and religious organizations like the Nigerian Canadian Muslim Congregation Calgary provide communal spaces for Nigerians. Common traditions include fasting and prayer for healing, and many Nigerians look to faith leaders for alleviating medical conditions. Conclusion: I learned about the Nigerian immigrant community through community ecosystem mapping and developed collaborative relationships with community leaders. I used this information to tailor my focus group questions to their cultural health beliefs and examine the clashes between Nigerian culture and the Canadian healthcare system.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".