The FORGE AHEAD clinical readiness consultation tool: a validated tool to assess clinical readiness for chronic disease care mobilization in Canada’s First Nations
Bibliographic record
Abstract
Abstract Background Given the astounding rates of diabetes and related complications, and the barriers to providing care present in Indigenous communities in Canada, intervention strategies that take into account contextual factors such as readiness to mobilize are needed to maximize improvements and increase the likelihood of success and sustainment. As part of the national FORGE AHEAD Program, we sought to develop, test and validate a clinical readiness consultation tool aimed at assessing the readiness of clinical teams working on-reserve in First Nations communities to participate in quality improvement (QI) to enhance diabetes care in Canada. Methods A literature review was conducted to identify existing readiness tools. The ABCD – SAT was adapted using a consensus approach that emphasized a community-based participatory approach and prioritized the knowledge and wisdom held by community members. The tool was piloted with a group of 16 people from 7 provinces and 11 partnering communities to assess language use, clarity, relevance, format, and ease of completion using examples. Internal reliability analysis and convergence validity were conducted with data from 53 clinical team members from 11 First Nations communities (3–5 per community) who have participated in the FORGE AHEAD program. Results The 27-page Clinical Readiness Consultation Tool (CRCT) consists of five main components, 21 sub-components, and 74 items that are aligned with the Expanded Chronic Care Model. Five-point Likert scale feedback from the pilot ranged from 3.25 to 4.5. Length of the tool was reported as a drawback but respondents noted that all the items were needed to provide a comprehensive picture of the healthcare system. Results for internal consistency showed that all sub-components except for two were within acceptable ranges (0.77–0.93). The Team Structure and Function sub-component scale had a moderately significant positive correlation with the validated Team Climate Inventory, r = 0.45, p
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".