Nature Prescriptions and Indigenous Peoples: A Qualitative Inquiry in the Northwest Territories, Canada
Bibliographic record
Abstract
Nature prescription programs have become more common within healthcare settings. Despite the health benefits of being in nature, nature prescriptions within the context of Indigenous Peoples have received little attention. We therefore sought to answer the following question: What are circumpolar-based physicians' and Indigenous Elders' views on nature prescribing in the Northwest Territories, Canada? We carried out thirteen semi-structured interviews with physicians between May 2022 and March 2023, and one sharing circle with Indigenous Elders in February 2023. Separate reflexive thematic analysis was carried out to generate key themes through inductive coding of the data. The main themes identified from the physician interviews included the importance of cultural context; barriers with nature prescriptions in the region; and the potential for nature prescriptions in the North. Reflections shared by the Elders included the need for things to be done in the right way; the sentiment that the Land is not just an experience but a way of life; and the importance of traditional food as a connection with Nature. With expanding nature prescription programs, key considerations are needed when serving Indigenous communities. Further investigation is warranted to ensure that nature prescriptions are appropriate within a given context, are inclusive of supporting Land-based approaches to health and wellbeing, and are considered within the context of Indigenous self-determination.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".