Systematic overviews of partnership principles and strategies identified from health research about spinal cord injury and related health conditions: A scoping review
Bibliographic record
Abstract
Scoping review. To identify and provide systematic overviews of partnership principles and strategies identified from health research about spinal cord injury (SCI) and related health conditions. Four health electronic databases (Medline, Embase, CINAHL, PsycINFO) were searched from inception to March 2019. We included articles that described, reflected, and/or evaluated one or more collaborative research activities in health research about SCI, stroke, multiple sclerosis, Parkinson’s disease, amputation, cerebral palsy, spina bifida, amyotrophic lateral sclerosis, acquired brain injury, or wheelchair-users. Partnership principles (i.e. norms or values) and strategies (i.e. observable actions) were extracted and analyzed using directed qualitative content analysis. We included 39 articles about SCI (n = 13), stroke (n = 15), multiple sclerosis (n = 5), amputation (n = 2), cerebral palsy (n = 2), Parkinson’s disease (n = 1), and wheelchair users (n = 1). We extracted 110 principles and synthesized them into 13 overarching principles. Principles related to building and maintaining relationships between researchers and research users were most frequently reported. We identified 32 strategies that could be applied at various phases of the research process and 26 strategies that were specific to a research phase (planning, conduct, or dissemination). We provided systematic overviews of principles and strategies for research partnerships. These could be used by researchers and research users who want to work in partnership to plan, conduct and/or disseminate their SCI research. The findings informed the development of the new SCI Integrated Knowledge Translation Guiding Principles (www.iktprinciples.com) and will support the implementation of these Principles within the SCI research system.
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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 | 0.104 | 0.282 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.056 | 0.051 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".