Exploring integrated tertiary care for children from Nunavut: experiences of families and healthcare providers at the Aakuluk clinic in Ottawa, Canada
Bibliographic record
Abstract
Children from circumpolar regions must travel long distances to southern tertiary care centres for specialised care. While there are initiatives underway to support care closer to home, medical travel remains a necessity for many families. The Aakuluk clinic has been operating since 2019 at a tertiary hospital in Ottawa, Canada, to provide care to children from Nunavut. The clinic team includes nurse case managers, physicians, social workers, interpreters, and several community partners. This project aimed to identify the strengths and the challenges of the clinic from the perspectives of parents and healthcare providers. The study was conducted in collaboration with healthcare professionals and community members and was guided by Inuit research approaches. Fifty-one participants (parents and healthcare providers) in Nunavut and Ottawa were interviewed. The main strengths and challenges of the clinic that were reported are related to the following themes: access to holistic care, supporting the role of Inuit professionals as part of the care team, and resources needed to continue offering programmes such as Aakuluk to Inuit families. From the perspectives of parents and healthcare providers, there are several components of the Aakuluk model that can be considered when developing services for Inuit families in other tertiary care centres.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".