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Record W2010550489 · doi:10.1080/10400435.2011.614677

International Mobility Technology Research: A Delphi Study to Identify Challenges and Compensatory Strategies

2011· article· en· W2010550489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAssistive Technology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsQueen's University
FundersVA Pittsburgh Healthcare SystemNational Science Foundation
KeywordsDelphi methodEngineeringKnowledge managementEngineering ethicsPsychologyEngineering managementComputer science

Abstract

fetched live from OpenAlex

We sought to identify logistical and ethical challenges to performing wheelchair-related research in low- and middle-income countries and to generate a list of compensatory strategies to address these challenges. Thirteen individuals with experience in the field participated in an online Delphi study. The surveys asked participants to identify research challenges, suggest strategies to address the selected challenges, and critique each other's strategies. Participants identified challenges in the use of research techniques, compensation for participation that does not result coercion, oral and written translation materials, funding for research, collaboration with local professionals, and "respect for persons." Effective international mobility research requires time, cultural sensitivity, collaboration, and careful planning. An understanding of these requirements can allow researchers to anticipate and compensate for common pitfalls of their work, thus making the research more productive and beneficial to subjects. Future research is required to verify the general effectiveness of compensatory strategies.

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.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.404
GPT teacher head0.535
Teacher spread0.131 · 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