Beyond the hospital walls: The lived experiences of Sidra's radiologists with home-based picture archiving and communication system during a global crisis
Bibliographic record
Abstract
Objective: This study explores the adaptation of radiologists at Sidra Medicine, Qatar, to the home-based picture archiving and communication system (HPACS) during the COVID-19 pandemic. Methods: This qualitative study used a phenomenological methodology to delve into the experiences of radiologists using HPACS, which emerged as a crucial tool for remote radiology practice during the pandemic. It highlights the perceived benefits, barriers, and challenges of using HPACS, and emphasizes its role in ensuring continuity of patient care and diagnostics while adhering to safety protocols. Results: The study reveals how HPACS facilitated work efficiency and safety, and also presented challenges such as workspace limitations and technical issues. The findings suggest a transformative impact of HPACS on the field of radiology, and indicate a future marked by increasingly digital and decentralized practices. Conclusion: This research contributes to understanding the adaptation of healthcare professionals to remote work technologies and provides insights for improving remote radiology systems and preparing for future crises.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".