Public Healthcare Project Appraisal in the United Arab Emirates: Towards Better Feasibility
Bibliographic record
Abstract
In the United Arab Emirates (UAE) there has been a tremendous improvement in levels of medical healthcare services over the past few years. There is a clear government vision to improve the healthcare services in all Emirates. This fact is supported by the establishment of the General Authority for Health Services for the Emirates of Abu Dhabi (GAHS). This research aims at investigating the role of the local government authorities and the private sector in the appraisal process of public healthcare projects in the Emirate of Abu Dhabi. It traces and discusses procedures currently used by involved authorities and bodies for procurement, cost budgeting and cost control during public healthcare project lifecycle in the UAE. Furthermore, the effectiveness of the design of organization management structure of different involved entities is traced, analysed and judged. The study is guided by a comprehensive literature review and a survey of cost data of several healthcare projects as well as interview sessions with senior engineers and personnel from public sector, industrial experts and construction managers involved in the healthcare project lifecycle. Two major factors combine to create the situation where UAE public healthcare projects suffer from cost and time overruns. First, the consequential changes arising from insufficient scope definitions and second, the lack of appropriate communication and coordination between the government bodies involved during the healthcare project. The study finally concludes with recommendations for improving the accuracy of the early cost estimating of UAE healthcare projects and their overall appraisal processes.
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 | 0.122 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".