Promoting Healthy Medication Use Through Indigenous Knowledge Sharing: A Coyote Story
Bibliographic record
Abstract
Polypharmacy is the administration of more medications than clinically required or appropriate, and it can negatively impact wellness. Prescribers, pharmacists, nurses, and those receiving care services all have an important role to play in promoting healthy medication use and minimizing the risk related to polypharmacy. Medication management involves health care professionals regularly reviewing drug therapies with patients for any needed changes. This strategy is a key way to reduce the harms of polypharmacy. A review of the First Nations Health Authority Health Benefits Claims data in 2015 confirmed that polypharmacy is an issue for First Nations in British Columbia, Canada. This was further validated in a series of meetings held in four First Nations communities. The learnings from these meetings were that many people do not know the names of their medications, the reasons for taking them, or how to advocate for themselves during health care interactions. A unique strategy was needed to both encourage and empower First Nations and Indigenous people to discuss managing their medications, and to support health care professionals to better understand how to engage First Nations patients about their medications.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".