Wiinaadmowing Etchi Piitzijig Enda’aat: Helping Elders Where They Live
Bibliographic record
Abstract
The response to the COVID-19 pandemic limited socializing and connecting, impacting the ability of Elders to pass on guidance and leadership through culturally specific ways. Community-level supports are vital for older adults living in First Nation communities, particularly supports that are inclusive, accessible, private, and confidential, and those that promote visiting, connecting, and culture, offer opportunities for outings, and use many avenues for awareness. Using an Anishinabek research methodology to inform decision-making among the Health and Community Wellness Committee, this community-based participatory action research study used the Gaataa’aabing visual research method to answer the research question: what are the perceptions of “community” adults over 50 years of age who have multiple chronic conditions about community-level supports during the COVID-19 pandemic? We recruited two participants who participated in three learning circles and contributed seven photos. We followed an adapted version of the collective consensual data analytic procedure to analyze over 400 coded segments, resulting in 15 themes organized into barriers to, thoughts and perceptions about, and strengths of community-level supports. We found that the importance of connecting through various means was heightened during the COVID-19 pandemic and community-level support aided in socialization through digital platforms. Although Elders enjoyed connecting with others through modern technology, they also felt that there is a need to return to the old ways, bringing back an Anishinabek way of life. Moving forward, service providers can use these findings to support the coordination of ‘friendly visiting’ via community-level volunteer programs to reduce the impacts of social isolation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".