Nexus of COVID-19 Crises and Health Care Performance in Jordan: The Moderation Role of Telemedicine, Innovation, and Infrastructure
Bibliographic record
Abstract
The ongoing COVID-19 pandemic in Jordan has brought terrifying effects on hospital management performance worldwide.The ultimate purpose is to generate empirical knowledge and open research opportunities into future studies.Investigating critical resourcebased factors would significantly contribute to the growing body of knowledge that informs policies and programs for mitigating the negative effects of the pandemic on the administration of Jordan's healthcare sector.To this end, we surveyed a sample of 418 managers in the Jordanian health sector.To produce numeric data and to test the hypotheses, the researchers employed structural equation modeling, specifically using the PLS-SEM technique.This study argues that the COVID-19 pandemic has had a profound negative impact on hospital performance in Jordan.Telemedicine, innovation, and infrastructure exhibit a significant and positive direct influence on management performance.As a result, this study accepts two hypotheses pertaining to the moderating influence of telemedicine and infrastructure in mitigating the negative consequences of COVID-19 on performance.However, the hypothesis related to the moderating role of innovation in the impact of COVID-19 on performance is rejected.The pandemic has presented unprecedented challenges to the healthcare sector, necessitating the development of effective management strategies to address the surge in patient volume and resource constraints.Telemedicine and healthcare infrastructure have been identified as critical resources with a significant moderating effect on healthcare management performance during this crisis.Telemedicine, as a technology-related resource, enables remote healthcare delivery, virtual consultations, and monitoring, which have become crucial during the pandemic.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".