Exploring Elders’ and Seniors’ Perceptions of How Climate Change is Impacting Health and Well-being in Rigolet, Nunatsiavut / ᕿᒥᕐᕈᓂᖅ ᐃᓐᓇᐃᑦ ᐊᒻᒪᓗ ᐃᓄᑐᖃᐃᑦ ᐃᓱᒪᔾᔪᓯᖏᓐᓂᒃ ᕆᒍᓚᑦ, ᓄᓇᑦᓯᐊᕗᒻᒥ ᓯᓚᐅᑉ ᐊᓯᔾᔨᐸᓪᓕᐊᓂᖓᓂᒃ ᐊᑦᑐᐃᓂᖃᖅᑎᓪᓗᒍ ᐃᓗᓯᕐᒥᒃ ᐊᒻᒪᓗ ᖃᓄᐃᓐᖏᓂᖏᓐᓂᒃ
Bibliographic record
Abstract
Climate changes are rapidly intensifying and can lead to adverse global health impacts. Indigenous populations are especially vulnerable to climate change because of their dependence on the environment for cultural activities and subsistence. The voices of Inuit Elders and seniors encompass deep wisdom and history; as such, the goal of this research was to examine the perceived impacts of climate and environmental changes on physical, mental, and emotional health, as observed by Elders and seniors in the Inuit community of Rigolet, Nunatsiavut, Labrador, Canada. A mixed-methods approach was used to gather data capturing these local observations, as well as perceived impacts on community health. A community survey was administered in November 2009 (n = 75) and in-depth interviews were conducted with Elders and seniors from January to October 2010 (n = 22). Survey results indicated that Elders and seniors observing changes in weather patterns, water systems, and wildlife were more likely to perceive climate change impacts on health (p < 0.05). Emergent themes from the interviews included: recurring observations of climate change, including changes in temperature, ice, snow, and seasonal timing; impacts on physical health, including reduced physical activity levels and poorer nutrition; impacts on mental and emotional health, including feelings of isolation and depression; and an identified need for community-wide adaptation. This research emphasized the importance of understanding Elder-specific perspectives of climate-health relationships in the Canadian North to develop sustainable, culturally relevant adaptation strategies to mitigate health impacts related to climate change.ᓯᓚᐅᑉ ᐊᓯᔾᔨᐸᓪᓕᐊᓂᖓ ᓱᒃᑲᓕᔪᒃᑯᑦ ᐱᕙᓪᓕᐊᕗᖅ ᐊᒻᒪᓗ ᓯᓚᕐᔪᐊᕐᒥ ᐊᑦᑕᓇᖅᑐᒃᑯᑦ ᐊᑦᑐᐃᔪᓐᓇᖅᑯᖅ ᐃᓗᓯᓕᕆᓂᒃᑯᑎᒍᑦ. ᓯᓚᕐᔪᐊᕐᒥ ᓄᓇᖃᖅᑳᖅᑑᔪᑦ ᐊᑦᑐᖅᑕᐅᓂᖅᐹᖑᔪᓐᓇᖅᑯᑦ ᐊᕙᑎᒥᓂᒃ ᑕᑎᖃᕐᓂᕐᒥᓄᑦ, ᐱᖅᑯᓯᖏᑎᒍᑦ ᐊᒻᒪᓗ ᐱᓇᓱᐊᖅᐸᓐᓂᖏᑎᒍᑦ. ᐃᓄᐃᑦ ᐃᓐᓇᐃᑦ ᐃᓐᓇᑐᖃᐃᓪᓗ ᓂᐱᖏᑦ ᓯᓚᑐᓂᕐᒥᒃ ᑐᓐᖓᕕᖃᖅᑯᑦ ᐊᒻᒪᓗ ᐊᑐᖅᓯᒪᔭᖃᖅᑯᑦ; ᓲᕐᓗ ᐆᒧᖓ ᖃᐅᔨᓴᕐᓂᕐᒧᑦ ᑐᕌᒐᖃᖅᑎᓪᓗᒍ ᓯᓚᐅᑉ ᐊᓯᔾᔨᐸᓪᓕᐊᓂᖓᓂᒃ ᐊᑦᑐᐃᔾᔪᑕᐅᔪᓂᒃ ᐊᒻᒪᓗ ᖃᓄᖅ ᐊᕙᑎᐅᑉ ᑕᐅᑦᑐᖓ, ᐃᓱᒪᒃᑯᑦ ᐊᒻᒪᓗ ᐃᓗᓯᓕᕆᓂᒃᑯᑎᒍᑦ, ᐃᓐᓇᐃᑦ ᑕᑯᓐᓇᖅᑕᖃᖅᑯᑦ ᐃᓄᐃᑦ ᓄᓇᓕᖓᓂ ᕆᒍᓚᑦ, ᓄᓇᑦᓯᐊᕗᒻᒥ, ᓛᐸᑐᐊᒥ, ᑲᓇᑕᒥ. ᑲᑎᑕᐹᓂᑦ ᖃᐅᔨᓴᕐᓂᕐᒧᑦ ᐊᖅᑯᑏᑦ ᓄᓇᓕᒻᒥ ᑕᑯᓐᓇᒐᐅᕗᑦ ᐊᒻᒪᓗ ᑕᑯᓐᓇᑕᒃᑯᑦ ᓄᓇᓕᒻᒥ ᐃᓗᓯᓕᕆᓂᒃᑯᑦ ᐊᑦᑐᖅᑕᐅᓂᐅᔪᑦ. ᓄᓇᓕᒻᒥ ᐊᐱᖅᓲᑎ ᓄᕕᐱᕆ 2009−ᒥ ᐃᖏᕐᕋᓂᖃᔪᕗᖅ (n = 75) ᐊᒻᒪᓗ ᐃᓗᑐᔪᓂᑦ ᐊᐱᖅᓲᑎᖃᔪᕗᑦ ᐃᓐᓇᕐᓄᑦ ᐃᓄᑐᖃᕐᓄᓪᓗ ᔮᓐᓄᐊᓕᒻᒥᑦ ᐅᑐᐱᕆᒧᑦ 2010−ᒥ (n = 22). ᐊᐱᖅᓲᑎᒧᑦ ᑭᒡᒍᓯᐅᔪᔪᑦ ᓇᓗᓇᐃᖅᓯᔪᕗᑦ ᐃᓄᑐᖃᐃᑦ ᑕᑯᔭᖃᖅᓯᒪᓂᖏᓐᓂᒃ ᓯᓚᐅᑉ ᖃᓄᐃᓐᓂᖓᑕ ᐊᓯᔾᔨᕐᓂᖏᓐᓂᑦ, ᐃᒪᐅᓪᓗ ᐊᒻᒪᓗ ᐆᒪᔪᐃᑦ ᑕᒪᒃᑯᐊ ᐊᑦᑐᐃᔪᑦ ᐃᓄᐃᑦ ᐃᓗᓯᕆᔭᖏᓐᓂᒃ (p < 0.05). ᓲᔪᕐᓇᑐᓪᓗ ᐊᓚᒃᑲᔪᔪᑦ ᐊᐱᖅᓲᑕᐅᔪᔪᓂᑦ ᐃᓗᓕᖃᐅᕐᓂᖓᓄᑦ ᒪᑯᓂᖓ: ᓲᔪᕆᔭᐅᕙᑦᑐᑦ ᓯᓚᐅᑉ ᐊᓯᔾᔨᕐᓂᖓᓄᑦ; ᓂᓪᓚᓱᓐᓂᕐᒧᑦ ᐆᖅᑰᓇᕐᓂᒧᓪᓗ; ᓂᓚᐅᑉ, ᐊᐳᑎᐅᑉ ᐊᒻᒪᓗ ᐅᐱᕐᖔᑉ, ᐅᑭᐅᑉ ᐊᓰᓐᓇᓕᓂᖏᓐᓂᒃ; ᐊᑦᑐᐃᔾᔪᑕᐅᔪᓪᓗᑎ ᑎᒥᒧᑦ ᐃᓗᓯᓕᕆᓂᕐᒧᑦ; ᐃᓱᒪᒧᑦ ᐊᒻᒪᓗ ᐃᓱᒪᒃᑯᑦ ᐃᓗᓯᓕᕆᓂᕐᒨᖓᔪᑦ, ᐃᓚᓕᐅᑦᑐᒋᑦ ᐃᑉᐱᓐᓂᐊᔾᔪᑎᑦ ᐃᓄᑑᓕᐅᑎᓂᕐᒧᑦ ᐊᒻᒪᓗ ᓄᒫᓱᓐᓂᒃᑯᑦ ᐊᒻᒪᓗ; ᑭᓐᖒᒪᑦᑎᓂᖅᓄᓇᓕᒻᒥ ᐊᓯᔾᔨᐸᓪᓕᐊᓂᕐᒧᑦ ᖃᓄᖅᑑᕈᑎᓂᒃ. ᐅᓇ ᖃᐅᔨᓴᕐᓂᖅ ᓇᓗᓇᐃᖅᓯᓚᐅᖅᑯᖅ ᐱᓪᓗᕆᑦᑐᓂᒃ ᑐᑭᓯᐅᒪᔾᔪᑎᓂᒃ ᐃᓐᓇᕐᓄᑦ ᐃᓱᒪᒋᔾᔪᑕᐅᓯᒪᔪᓂᒃ ᓯᓚᐅᑉ ᐊᓯᔾᔨᐸᓪᓕᐊᓂᖓᓄᑦ ᐃᓗᓯᓕᕆᓂᕐᒧᑦ ᐊᑦᑐᐊᓂᖃᖅᑎᓪᓗᒋᑦ ᑲᓇᑕᐅᑉ ᐅᑭᐅᖅᑕᖅᑐᖓᓂ ᑐᕌᒐᖃᕐᓂᐊᑎᓪᓗᒋᑦ ᑲᔪᓯᐅᒪᔾᔪᑎᔅᓴᓂᒃ, ᐱᖅᑯᓯᖅᑎᒍᑦ ᐊᑲᕐᕆᔪᓂᒃ ᐅᐸᓗᖓᐃᔭᕈᑎᓂᒃ ᐊᑦᑐᖅᑕᐅᓂᑭᓐᓂᖅᓴᐅᖁᓪᓗᒋᑦ ᐃᓗᓯᓕᕆᓂᒃᑯᑦ ᐃᑉᐱᓐᓂᕈᑎᓂᒃ ᓯᓚ ᐊᓯᔾᔨᐸᓪᓕᐊᑎᓪᓗᒍ.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".