The Challenges and Advantages of Implementing a Lean-Led Design Approach
Bibliographic record
Abstract
Healthcare projects, like other complex projects, begin with a project definition phase, where client needs are identified, and design solutions are proposed. All decisions related to this phase have an important impact on workspace conditions. Nevertheless, traditional methods of project definition management have been proven to be inadequate. An ill-defined project might lead to an increase in hospital-acquired infections or patient mortality. Participatory approaches such as Lean-led Design—in which clients including users play an important role from the beginning—are proposed to address this problem. This paper aims to identify and analyze the advantages and difficulties of Lean-led Design during the project definition process. A single case study was used to explore these issues. The case study chosen was a mega Canadian hospital project that implemented a Lean-led Design approach. Data were collected using archive research and semistructured interviews. This paper will help AEC industry stakeholders to understand the advantages and challenges involved in implementing a Lean-led Design approach. The findings of this study could help architects as well as managers to concentrate their efforts on significantly relevant issues.
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 | 0.102 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".