Solutions to Overcome Integrated Project Delivery Implementation Barriers: A Meta-Synthesis Approach
Bibliographic record
Abstract
Construction projects encounter myriad problems, some of which may be connected to the project delivery model. Integrated project delivery (IPD) is an approach that removes the gap between the planning and the construction stages of a project. Various barriers to implementation exist within the construction industry and these can be resolved by effective solutions. Identifying and classifying these solutions is considered essential for successful project delivery. In this context, this study aims to illustrate and classify the solutions that have been proposed since the introduction of IPD as a new approach for the implementation of construction projects. In this study, a meta-synthesis approach has been used as a qualitative method, and pattern and descriptive coding and analysis have been used to analyse the data. The solutions analysed in the meta-synthesis suggest that all stakeholders—including designers, construction engineers, construction team members and operation and maintenance team members—each have the same responsibility to improve IPD and meet the project goals. This study is significant because it suggests important resolutions to the barriers to IPD implementation and may help construction industry stakeholders better facilitate IPD and enhance clauses of their contracts.
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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 | 0.097 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".