The Nak’azdli Whu’ten is a First Nations community located in northern British Columbia Canada. They have prioritized support for the mental health and wellbeing of their Elders and were keen to strengthen intergenerational linkages in the community to preserve cultural wisdom held by the elders for future generations. We co-created a digital storytelling workshop using technology to facilitate knowledge-sharing between Elders and youths. This pilot 10 session workshop involved all grade 6 and 7 students at a First Nations school and 20 First Nations Elders. Students recorded the Elders who orally shared stories and then added imagery and sounds to capture their understandings and create a digital story. The workshop was led by elders. Our project demonstrates one way to document oral histories while simultaneously building intergenerational relationships. We will discuss how this project successfully fostered intergenerational relationships, helped preserve cultural identity, and reduced social isolation of First Nations Elders.
Bibliographic record
Abstract
This study aims to empower community-dwelling older adults for addressing falls risk and falls prevention and to make informed health decisions. The framework is guided by the WHO falls preventive model which include three pillars: 1) developing awareness of the importance of fall prevention; 2) enhancing the appraisal of falls risk factors; and 3) promoting culturally appropriated evidence based interventions (WHO, 2007). This study provides the opportunity for older adults to share their experiences, learn from the experiences of others, take an active role in preventing falls based on their health status and context, keep track of their outcomes, and give input into prevention strategies. However, due to liability issues, promoting older adults’ independence and empowerment may not follow the same direction in the health care facilities, which put high priority on safety and less emphasis on allowing older adults to make their own choices.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".