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Record W4366347123 · doi:10.1038/s41393-023-00897-z

Strengths, gaps, and future directions on the landscape of ethics-related research for spinal cord injury

2023· review· en· W4366347123 on OpenAlexafffund
Anna Nuechterlein, Lydia Feng, Alaa Yehia, Judy Illes

Bibliographic record

VenueSpinal Cord · 2023
Typereview
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of British Columbia HospitalNeuroDevNetUniversity of British Columbia
FundersGovernment of Canada
KeywordsContext (archaeology)Spinal cord injuryEthnic groupMedicineInclusion (mineral)DemographicsSituatedValue (mathematics)Research ethicsRace (biology)Identity (music)Gender studiesSociologyDemographyAestheticsPsychiatryGeographyAnthropology

Abstract

fetched live from OpenAlex

Spinal cord injury (SCI) affects between 250,000-500,000 people globally each year. While the medical aspects of SCI have received considerable attention in the academic literature, discourse pertaining to its ethical implications is more limited. The experience of SCI is shaped by intersecting demographic and identity factors such as gender, race, and culture that necessitate an intersectional and value-based approach to ethics-related research that is properly situated in context. Given this background, we conducted a content analysis of academic studies exploring the perspectives and priorities of individuals with SCI published in peer-reviewed journals in the decade between 2012-2021. Terms pertaining to SCI and ethics were combined in a search of two major publication databases. We documented overall publication patterns, recruitment and research methods, reporting of demographic variables, and ethics-related discourse. Seventy (70) papers met inclusion criteria and were categorized by their major foci. Findings reveal a gap in reporting of participant demographics, particularly with respect to race and ethnicity, geographic background, and household income. We discuss these person-centered themes and gaps that must be closed in the reporting and supporting of SCI research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.348
GPT teacher head0.558
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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