Improving food security for Labrador Inuit in Nunatsiavut, Labrador: A matter of health equity
Bibliographic record
Abstract
Abstract Background The current low state of food security amongst Inuit in Canada is influenced by policy choices. Policy actors develop and implement policies, yet few research studies include their perspectives. This study includes policy actors’ perspectives on gaps and areas of improvement for policies that pertain to food security for Labrador Inuit in Nunatsiavut. Nunatsiavut is one of the four Inuit land claim areas in northern Canada making up the Inuit homeland, or Inuit Nunangat. It is situated in northern Labrador in the province of Newfoundland and Labrador, Canada. Methods This qualitative study consisted of key informant interviews conducted from July 2020-December 2020 with policy actors that spanned the Nunatsiavut Government (regional Inuit government), Government of Newfoundland and Labrador (provincial government), the Government of Canada (federal government), non-governmental organizations and private industry. Participants were asked about their role, policy gaps and opportunities for improving policies that pertain to food security in Nunatsiavut. It also included initial insights from emergency food security measures implemented during the first wave of COVID-19 in 2020. Results Fifteen key informant interviews were completed, and three participants provided written responses. The results were reported as per the consolidated criteria for reporting qualitative studies (COREQ): 32–item checklist. Seven participants (39%) stated they developed policy, six participants (33%) stated they both developed and implemented policy and five participants (28%) stated they implemented policy. Seven themes were identified from discussions with policy actors. Policy recommendations to improve food security include improving transportation, social policies, and policy coherence in policy implementation. Five separate themes were identified from discussions on implementing emergency food security measures during the first wave of COVID-19 in Nunatsiavut. These included inadequacy of social policies, hidden poverty among people living in Nunatsiavut and future considerations for post- COVID-19 food security policies. Conclusion The results of this study show that improving food security in Nunatsiavut is a matter of health equity. During COVID-19, these inequities were further highlighted, demonstrating the importance of urgent action. Findings from this study can inform actions to improve existing and future policies that pertain to food security for Labrador Inuit in Nunatsiavut.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".