Investigating the Organizational and the Environmental Issues that Influence the Adoption of Healthcare Information Systems in Public Hospitals of Iraq
Bibliographic record
Abstract
Healthcare information systems (HIS) are an important part of nowadays hospitals as it provides valuable benefits and functionalities for healthcare provision. However, the implementation and adoption of these complex innovations is a challenging task as documented by the literature; therefore, careful planning and consideration to all important factors that influence the adoption process by healthcare staff is required. Governmental reports stated that the usage of HIS systems within public hospitals of Iraq is still low and problematic; that’s why the current study aims at empirically investigating the opinions of healthcare staff regarding the adoption of HIS within public hospitals of Iraq. The current study extended the UTAUT model by integrating additional organizational and environmental factors and for that purpose a questionnaire was developed for obtaining the healthcare staff’s opinions. To the best of our knowledge, this is the first empirical study that utilized the UTAUT model to tackle the topic of HIS adoption in Iraq public healthcare sector. The study was able to explain 33% and 46% of the variance within the behavioral intention and the usage of HIS, respectively. The practical findings of this quantitative study can be helpful for healthcare officials to address the actual challenges related to HIS adoption and to set proper strategies for implementing futuristic HIS 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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".