A field-test of Not Deciding Alone to support Inuit with health decision making: co-production of a mixed methods study guided by aajiiqatigiingniq
Bibliographic record
Abstract
Shared decision-making supports person-centred care. Our team of Inuit-led and/or -focused organizations and researchers field-tested a strategy called Not Deciding Alone to support health decision-making. Guided by aajiiqatigiingniq, a principle of collective decision-making and consensus-building, we co-produced a mixed-methods study to: (1) train Qikiqtani region community health representatives (CHRs) with a workshop, (2) develop a radio show and survey, and (3) assess the radio show with Inuit community members in the health system. We evaluated participant experiences using forms, case studies, and an online survey. The workshop was delivered to 13 CHRs; seven (54%) provided evaluation data. All (100%) reported positive experiences with the content, activities, and facilitation. One (14%) said the workshop was too short; four (57%) agreed there was enough discussion time. Six (86%) reported new learning. Three radio show events were held with 33 survey respondents, the majority women (n = 25, 76%). Most found the show informative (n = 29, 88%) and helpful for future decision-making (n = 27, 82%), and said it would improve their confidence (n = 27, 82%). Not Deciding Alone was found to be an acceptable, useful, and relevant strategy for supporting health decision-making among Inuit community members.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.246 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".