Facilitators and Barriers to International Collaboration in Spinal Cord Injury: Results from a Survey of Clinicians and Researchers
Bibliographic record
Abstract
International collaboration in spinal cord injury (SCI) research is necessary to overcome the challenges often encountered by clinicians and researchers, including participant recruitment, high cost, and the need for specialized expertise. However, international collaboration poses its own obstacles. The objective of this study was to conduct an international online survey to assess barriers and facilitators to international SCI clinical research, potential initiatives to facilitate future collaborations, and the use of SCI-specific data sets and standards. Results were analyzed using descriptive statistics. Of 364 total respondents, 213 completed the survey, with the majority of these participants based in North America (38%), Asia (22%), Europe (18%), and Oceania (16%). Over half had more than 10 years of experience in SCI research or clinical practice (57%) and 60% had previous experience with international collaborations. Funding was identified as a top barrier (82%), a facilitator (93%), and a proposed future initiative (97%). Communication and technology were also identified as strong facilitators and proposed future initiatives. The International Standards for Neurological Classification of SCI were used by 69% of participants, the International Standards to document remaining Autonomic Function after SCI by 13% of participants, and the International SCI Data Sets by 45% of participants. As the need for international collaborations in SCI research increases, it is important to identify how clinicians and researchers can be supported by SCI consumer and professional organizations, funders, and networks. Furthermore, unique solutions to overcome modifiable barriers and creation of new facilitators are also needed.
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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 | 0.042 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".