Bridges of perspectives: representation of people with lived experience of spinal cord injury in editorial boards and peer review
Bibliographic record
Abstract
BACKGROUND: Diversity among editorial boards and in the peer review process maximizes the likelihood that the dissemination of reported results is both relevant and respectful to readers and end users. Past studies have examined diversity among editorial board members and reviewers for factors such as gender, geographic location, and race, but limited research has explored the representation of people with disabilities. Here, we sought to understand the landscape of inclusivity of people with lived experience of spinal cord injury specifically in journals publishing papers (2012-2022) on their quality of life. METHODS: An open and closed 12-question adaptive survey was disseminated to 31 journal editors over a one-month period beginning December 2022. RESULTS: We received 10 fully completed and 5 partially completed survey responses (response rate 48%). Notwithstanding the small sample, over 50% (8/15) of respondents indicated that their journal review practices involve people with lived experience of spinal cord injury, signaling positive even if incomplete inclusivity practices. The most notable reported barriers to achieving this goal related to identifying and recruiting people with lived experience to serve in the review and editorial process. CONCLUSIONS: In this study we found positive but incomplete trends toward inclusivity in journal practices involving people with lived experience of spinal cord injury. We recommend, therefore, that explicit and genuine efforts are directed toward recruitment through community-based channels. To improve representation even further, we suggest that editors and reviewers be offered the opportunity to self-identify as living with a disability without discrimination or bias.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | MetaresearchScholarly communication Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.048 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".