Mapping Stakeholder Perspectives on Engagement in Concussion Research to Theory
Bibliographic record
Abstract
BACKGROUND: Involving stakeholders has been acknowledged as a way to improve quality and relevance in health research. The mechanisms that support effective research engagement with stakeholders have not been studied in the area of concussion. Concussion is a large public health concern worldwide with billions of dollars spent on health care services and research with improvements in care and service delivery not moving forward as quickly as desired. Enabling effective stakeholder engagement could improve concussion research and care. OBJECTIVE: The aim of the study was to identify potential benefits, challenges, and motivators to engaging in research by gathering the perspectives of adults with lived experience of concussion. METHODS: A thematic analysis of qualitative responses collected from a convenience sample attending a provincial brain injury conference (n = 60) was undertaken using open coding followed by axial coding. RESULTS: Four themes regarding benefits to engagement emerged: first-hand account, meaningful recovery, research relevance, and better understanding of gaps. Three forces inhibited engagement: environmental barriers, injury-related constraints, and personal deterrents. Four enablers supported engagement: focus on positive impact, build connections, create a supportive environment, and provide financial assistance. CONCLUSIONS: Understanding stakeholder's perspectives on research engagement is an important issue that may serve to improve research quality. There may be unique nuances at play with injury-specific stakeholders that require researchers to consider a balance between reducing inhibitors while supporting enablers. These findings are preliminary and limited. Nevertheless, they provide needed insight and guidance for ongoing investigation regarding improvement of stakeholder engagement in concussion research.
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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 | 0.122 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.025 | 0.044 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".