Online community discourse on spinal cord injury research
Bibliographic record
Abstract
BACKGROUND: Public perceptions of spinal cord injury (SCI) research can influence trust in scientific advancements and therapeutic interventions. Social media is a useful tool to provide insight into these perceptions and related values. This study examines comments to posts pertaining to SCI research reports on a widely used social media platform. METHOD: We used search term 'spinal cord injury' to collect posts and associated top-level comments spanning 2016-2023 from Reddit. Posts were included if they contained research reports on one of the following SCI interventions: biologic and synthetic materials; devices and technologies; physical and behavioral interventions; and pharmacological treatments. Each unique comment per Reddit post was coded for user self-identification, format, intervention, topic, and tone. RESULTS: Key topics of interest were: scientific progress (52%, 514/994), study details (30%, 301/994), and ethical implications (24%, 237/994) across interventions. Comments were generally neutral in tone. Fifty-four comments were made by users who self-identified as persons with lived experience of a spinal cord-related condition. Ethics-related comments (237/994) were focused on the themes of access (35%, 84/237) and beneficence (24%, 58/237). CONCLUSION: The SCI community is actively using the social media platform Reddit to seek information about research and its ethical dimensions. Across users, a significant proportion of comments are on research progress, ethics and study information. The largest proportion of ethics-focused comments by self-identifiers are on agency, and then equally on access, values, and resilience; ethics-related comments by non-self-identifiers focus on access, and beneficence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".