Understanding Fall-Risk Factors for Inuvialuit Elders in Inuvik, Northwest Territories, Canada
Bibliographic record
Abstract
Older Indigenous adults in Canada experience disproportionately poorer health outcomes than older non-Indigenous adults. Current fall-prevention literature suggests that older Indigenous adults have higher rates of falls and fall-related injuries; however, no information exists on older Inuit adults’ experience with falls. Using the social determinants of Inuit health (SDoIH) as a conceptual framework, this research sought to understand which of the SDoIH are believed by stakeholders (i.e., local fall prevention programmers [LFPPs] and Inuvialuit Elders) to affect most the likelihood of older Inuvialuit adults’ falls. The findings from the 12 semi-structured interviews and participant observations show that factors related to personal health status and conditions, personal health practices and coping skills, physical environments, social support networks, and access to health services increase older Inuvialuit adults’ likelihood of experiencing a fall. Some determinants, however, decrease their likelihood of experiencing falls (health practices, coping skills, and access to health services), and others, such as culture, were perceived as having little influence on falls. Specific cultural practices were identified as factors that influence the likelihood of older Inuvialuit adults experiencing a fall; however, the overall Inuvialuit culture was not. In light of these findings, we offer recommendations for LFPPs in Inuvik to implement fall-prevention programs that adequately address the SDoIH influencing older Inuvialuit adults’ fall risk and rates.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".