Bibliographic record
Abstract
The application of ergonomics is important when considering the built environment. In order to create an environment where form follows function, a detailed understanding of the tasks performed by the individuals who will live and work in the facility is required. Early involvement in the project is key to maximizing the benefit of ergonomics. At Sunnybrook and Women's College Health Sciences Centre in Toronto, Canada, this early intervention was embraced during the design process of a behavioural care unit for aggressive patients. The ergonomist was involved in three phases of design; user needs analysis, block schematics and detailed design. The user needs and characteristics were established using a combination of focus groups, interviews, direct observation, task analysis and critique of current working environments. The challenge was to present the information to the design team in a useful manner. The format chosen was a modification of Userfit (Poulson 1996) that outlined the various characteristics of the patient group and the design consequences with “what does this mean for me” statements. During the block schematics phase an iterative design process was used to ensure that the ergonomic principles and the user needs were incorporated into the design. Ergonomic input was used in determining the room sizes and layout and to ensure work processes were considered. Simple mock-ups and anthropometric data assisted in illustrating the need for design changes. Examples that highlight the areas of greatest impact of ergonomic intervention include the patient bathrooms, showers and tub room. Significant changes were made to the design to improve the safety of the work and living space of the end users. One of the greatest challenges was having an appreciation for the individual goals of the team members. Ensuring there was adequate space for equipment and staff often resulted in recommendations for increased space. This in turn would increase the cost of the project. The architect and, later in the project, the engineer had goals of bringing the project in on budget. The final design was very much a team effort and truly die result of an iterative process. The sum of the individual contributions could not match the combined efforts. It was only through the ergonomic contributions in this early design phase that the needs of the staff, patients and families could be so well represented. The success of the iterative process provides the foundation for bringing ergonomics considerations into the early design stages of future projects.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".