Bibliographic record
Abstract
It is well-established that Indigenous Peoples continue to experience a lower level of health than non-Indigenous Peoples in Canada. For many health leaders, finding practical strategies to close the gap in health disparities remains elusive. In this retrospective study, we will illustrate our own experience of transformational change using design and systems thinking tools toward a primary outcome of multi-stakeholder alignment. Using this approach enabled three Indigenous Provincial/Territorial Organizations (IPTOs) representing more than 150 First Nations communities from Saskatchewan, Manitoba, and Ontario to establish the largest community-led, collaborative approach to health transformation in Canada at the time. These IPTOs have gone on to pursue some of the most ambitious health transformation initiatives in Canada and in September 2018, were granted $68 million in funding support by the Government of Canada. If health leaders are looking at an alternative approach to closing the gap in Indigenous health, alignment thinking has shown promising results.
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 | 0.225 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.009 |
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".