Information Technology Governance Domains in Hospitals: A Case Study in Iran
Bibliographic record
Abstract
IT governance is a set of organizational structures ensuring decision-making rights and responsibilities with regard to the organization's IT assets. This qualitative study was carried out to identify the IT governance domains in teaching hospitals affiliated to Iran University of Medical Sciences. There were 10 heads of IT departments and 10 hospital directors. Semi structured interviews used for data collection. To analyze the data content analysis was applied. All the interviewees (100%) believed that decisions upon hospital software needs could be made in a decentralized fashion by the IT department of the university. Most of the interviewees (90%) believed that there were policies for logistics and maintenance of networks, purchase and maintenance, standards and general policies in the direction of the policies of the ministry of health and medical education. About 80% of the interviewees believed that the current emphasis of the hospital's IT unit and the hospital management for outsourcing of services were in the format of specialized contracts and under supervision of the university Statistic and IT department. A hospital strategic committee is an official organizational group consisting of hospital executives, heads of IT and multiple functional areas and business units in a hospital. In this committee, "the head of hospital" acts as the director of IT activities and ensures that IT strategies are alignment with the hospital business strategies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".