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Record W1760808396 · doi:10.3233/wor-2012-0017-5274

Anticipating needs and designing new items rapidly - a case study for the design of postural aid equipment

2012· article· en· W1760808396 on OpenAlexaff
Marie-Claude Prévost, Daniel P. Spooner

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsTimelineTask (project management)Engineering design processProduct (mathematics)Computer scienceSoftwareProduct designProcess (computing)Software engineeringEngineering managementHuman–computer interactionEngineeringSystems engineering

Abstract

fetched live from OpenAlex

In this case study, designers proactively proposed new product ideas to a client by using an ergonomic approach. This approach differs from a more traditional approach where one works within a specific, clientdefined project. The methodology used included basic ergonomic techniques such as task analysis and information gathering sessions conducted with users. It was adapted so that these enriched user sessions could be conducted within a short time period. After meeting with five users in seven days, designers identified 20 problems that could be tackled and eight design ideas that could be implemented over the short, medium and long term. The ideas encompassed a wide range of potential projects, including physical product improvements, new product lines, Web-site and software improvements and longer term research. Problems identified and ideas generated involved many disciplines including occupational therapy, mechanical engineering, graphical design, software engineering, sales and manufacturing know-how. This wide range was possible because designers were not constrained to specific project scopes and timelines. The client was involved in the idea evaluation process. As a result of this study two new projects were initiated so far.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.062
GPT teacher head0.264
Teacher spread0.202 · 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 designQualitative
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

Citations1
Published2012
Admission routes1
Has abstractyes

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