Ripples in the water: a toolkit for Aboriginal people on hemodialysis.
Bibliographic record
Abstract
In 2004-2005, the authors were engaged in a community-based research study with people of Elsipogtog First Nation to determine the causes of and solutions to non-adherence among community members with chronic kidney disease. This study highlighted the need for a toolkit intended for Aboriginal people who are required to undergo hemodialysis at a dialysis unit in a city away from their rural community, so that they are sufficiently educated, supported and resourced to access and experience culturally relevant health care. This paper presents the findings of a two-year community-based research study to develop the prototype or model for such a toolkit. The research involved meeting with nine community members in group meetings at least monthly over the two years to determine what such a toolkit should include and how it should best be presented. It also entailed an extensive review of relevant literature and relevant educational materials, as well as individual interviews with key stakeholders. The project resulted in a culturally relevant toolkit that can be staged according to people's readiness for the information and that fosters collaborative discussions between patients, family members and health care practitioners.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".