Anticipating needs and designing new items rapidly - a case study for the design of postural aid equipment
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".