Impacts of Cultural and Social Factors on Cost and Time in Management of Engineering, Procurement and Construction Projects: A Case Study
Bibliographic record
Abstract
Indigenous communities in Canada face significant barriers to accessing healthcare infrastructures that align with their cultural and social preferences. Existing healthcare construction project managements often overlook this problem with Indigenous communities, resulting in designs that fail to foster trust, inclusivity, and accessibility. This thesis study was motivated by tackling this problem with the proposition that considering social and cultural preferences to healthcare facilities of Indigenous communities is important to the success of such construction projects. It is noted that such projects have three stakeholders, namely Engineering (E) including design, Procurement (P), and Construction (C), EPC for short, and they may not be under the same managerial governance. The main methodology taken in this study is to build a simulation system or simulator for decision making in EPC project management by using the tool called System Dynamis (SD), initially developed at Sloan Management School at MIT, and to conduct a case study – construction of a hospital in an Indigenous community. The SD model evaluates the feasibility of incorporating Indigenous design elements—such as natural light, circular layouts, and eco-friendly materials—while maintaining cost efficiency, time management, and resource optimization. The model dynamically simulates interactions between variables such as material usage, energy efficiency, design stability, stakeholder coordination, and performance indices over a 24-month construction timeline. Simulation results reveal that timber is the most effective construction material for meeting Indigenous preferences, excelling in waste reduction, energy efficiency, design stability, cost savings, and schedule adherence. Timber also supports sustainable practices and enhances stakeholder collaboration, making it a viable choice for culturally responsive healthcare infrastructures. Conversely, concrete and steel exhibit higher material waste, lower energy efficiency, and reduced performance indices, creating challenges in cost and schedule compliance. The findings emphasize the importance of early engagement with Indigenous communities to integrate their preferences and perspectives, fostering collaboration and respect. This approach ensures project outcomes align with cultural and social expectations while promoting equity and inclusivity. Furthermore, the study contributes to advancing EPC project managements by illustrating how Indigenous preferences and sustainable practices can improve healthcare infrastructures development, ultimately enhancing accessibility and resilience in Indigenous communities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".