A PROGRESS-driven approach to cognitive outcomes after traumatic brain injury: A study protocol for advancing equity, diversity, and inclusion through knowledge synthesis and mobilization
Bibliographic record
Abstract
Evidence syntheses for advancing equitable traumatic brain injury (TBI) research, policy, and practice presents formidable challenges. Research and clinical frameworks are currently not specific to equity, diversity, and inclusion considerations, despite evidence that persons with TBI live in societies in which power imbalances and systems of social dominance may privilege some people and marginalize others. The present protocol outlines a strategy for a research program, supported by the Canadian Institutes of Health Research, that explores the integration of PROGRESS-Plus parameters in research with the goal of advancing open-science databases and tools to improve our understanding of equity in cognitive and brain health outcomes in TBI. PROGRESS-Plus is a framework outlining social, economic, and cultural parameters that may influence health opportunities and outcomes (e.g., place of residence, race, occupation, gender, etc.). A multistep research program is proposed to support three objectives: (1) organizing existing data on TBI-induced changes in cognition and brain health into a template to facilitate future research, including research using machine learning techniques; (2) updating published evidence with a more rigorous approach to the consideration of PROGRESS-Plus parameters; and (3) mobilizing knowledge on the current state of evidence that is relevant, equitable, and accessible. This program facilitates partnerships with knowledge users across clinical, research, academic, and community sectors to address the three research objectives through a unifying workflow of exchange, synthesis, and knowledge mobilization. We anticipate that this global collaboration between topic experts and community leaders in equity in brain health will add significant value to the field of TBI by promoting equity-transformative advancements in knowledge synthesis, policy, and practice.
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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 | 0.266 | 0.193 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".