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Record W4400985529 · doi:10.1038/s43856-024-00577-w

Healthcare can learn from space exploration to champion disability inclusion

2024· article· en· W4400985529 on OpenAlexaff
Farhan M. Asrar, Dana Bolles, Thu Jennifer Ngo‐Anh

Bibliographic record

VenueCommunications Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInclusion (mineral)ChampionEquity (law)Health careSpace (punctuation)Diversity (politics)Health professionalsPublic relationsPsychologyEconomic growthSociologyPolitical scienceComputer scienceSocial psychologyEconomics

Abstract

fetched live from OpenAlex

People with disabilities, including healthcare professionals, encounter many obstacles. The space sector is taking steps towards promoting equity, diversity, inclusion and accessibility, including developing the world’s first parastronaut program. Here, we propose that healthcare can learn from space in enhancing disability inclusion. Asrar et al. discuss the steps that the space sector is taking towards promoting equity, diversity, inclusion and accessibility, such as the world’s first parastronaut program. They propose that healthcare can learn from the space sector in enhancing disability inclusion and support for people, including healthcare workers, with disabilities.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.869
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.081
GPT teacher head0.401
Teacher spread0.319 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations5
Published2024
Admission routes1
Has abstractyes

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