Principles and related strategies for spinal cord injury research partnership approaches: a qualitative study
Bibliographic record
Abstract
Background: Conducting and/or disseminating research in partnership with potential research users is a popular approach to conducting useful and relevant research. Despite calls for guidance to support these research partnerships, evidence-based tools and resources remain limited. Aims and objectives: This study aimed to explore principles and related strategies for conducting and/or disseminating spinal cord injury (SCI) research in partnership with the SCI community, in order to gain insight into ways to support SCI research partnerships. This qualitative study included ten semi-structured interviews with SCI research partnership champions. The interviews focused on participants’ experiences with SCI research projects that are conducted or disseminated in partnership, and related principles and strategies to work in research partnerships. Participants mainly talked about principles related to: (1) the relationship between researchers and research users (for example, respect each other, avoid tokenism); (2) co-production of knowledge (for example, research user engagement early and throughout); and (3) meaningful engagement (for example, allowing flexibility). Examples of related strategies included attending collaborative conferences, research user engagement in refinement of research questions, training in research methods, and hiring people with SCI as part of the research team. Key conclusions: This qualitative study presents research partnership principles (norms) and related strategies (observable actions). This study can provide guidance for other researchers and research users who want to engage in (SCI) research partnerships. The findings of this study could be used to inform the development of evidence-based tools and resources to support future research partnerships.
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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 | 0.100 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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, 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".