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Record W2330135733 · doi:10.1177/0034355214568527

Minority Entity Disability, Health, Independent Living, and Rehabilitation Research Productivity Facilitators

2015· article· en· W2330135733 on OpenAlexfundno aff
Corey L. Moore, Fariborz Aref, Edward O. Manyibe, Evia Davis

Bibliographic record

VenueRehabilitation Counseling Bulletin · 2015
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
FundersNational Institute on Disability and Rehabilitation ResearchMcGill UniversityU.S. Department of Defense
KeywordsAgency (philosophy)ProductivityContext (archaeology)Medical educationFunding AgencyPsychologyRehabilitation counselingRehabilitationPublic relationsPolitical scienceMedicineSociologyEconomic growthSocial science

Abstract

fetched live from OpenAlex

The U.S. federal research agency’s (i.e., National Institute on Disability and Rehabilitation Research [NIDRR], National Institutes of Health [NIH]) sponsored research capacity building (RCB) efforts in the field of disability, health, independent living, and rehabilitation have historically focused on individual research skill building activities (e.g., postdoctoral fellowships, advanced research methods and statistics courses, grant-writing workshops) as a main intervention to facilitate increased research productivity among investigators. However, investigators’ personal intrinsic attributes as well as federal research agency policy and systems context are rarely considered as research productivity facilitators. On trend, minority entity (ME) RCB efforts tend to focus on addressing a single challenge, research skill building, while oftentimes neglecting the importance of intrinsic factors and federal agency policy and systems context. The purpose of this review was to synthesize the available peer review and gray literature, and policy on factors that facilitate investigators’ research productivity. Recommendations for advancing the current state of the science on research productivity facilitators are presented.

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)0.050
metaresearch head score (Gemma)0.400
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.400
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.112
GPT teacher head0.436
Teacher spread0.324 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2015
Admission routes1
Has abstractyes

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