Bibliographic record
Abstract
The Red River Jig (RRJ) is a traditional Métis dance practiced among Indigenous and non- Indigenous Peoples. Though exercise benefits physical health and fitness, the benefits of cultural dances to improve wholistic aspects of health are less clear. This study aimed to investigate social, psychosocial (cultural and mental), physical function, and physical fitness benefits of participation in an RRJ intervention. Indigenous and Non-Indigenous adults (N=27,38±14 years, 20 females) completed an 8-week RRJ intervention. Social support index, multigroup ethnic identity measures of cultural connectedness, and mental well-being questionnaires, as well as seated blood pressure and heart rate, weight, grip strength, six-minute walk test, and one-leg and tandem balance measures were assessed before and after the intervention. Cultural connectedness and mental well-being scores were similar before and after the intervention. Community components of social support scores (7.7 ± 1.4 vs. 10.3 ± 1.4, p<0.001), six-minute walk test distance (551.5 ± 96.6 m vs. 581.3 ± 100.8 m, p=0.002), and tandem eyes closed balance (86.0 ± 45.9 s vs. 102.8 ± 31.9 s, p=0.02) increased after the intervention. Seated systolic blood pressure (117.6 ± 6.1 mmHg vs. 112.9 ± 10.8 mmHg, p=0.02) and heart rate (62.2 ± 6.3 beats·min -1 vs. 59.8 ± 5.6 beats·min -1 , p=0.02), weight (80.6 ±23.1 kg vs. 79.4 ± 23.2 kg, p=0.04), and one leg eyes closed balance (26.0 ± 17.2 s vs. 22.6 ±16.3 s, p=0.04) decreased after the intervention. A RRJ intervention can improve social support, physical function, and physical fitness for Indigenous and Non-Indigenous adults. (Funding: Saskatchewan Health Research Foundation)
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.111 | 0.040 |
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".