Satisfaction and Experience of Palliative Patients with 24/7 Hotline Service During the COVID-19 Pandemic in Saudi Arabia
Bibliographic record
Abstract
Background: The COVID-19 pandemic prompted a number of shifts on healthcare. Conventional face-to-face visits were shifted during lockdown to virtual ones. Palliative care (PC) virtual visits have had high satisfaction rates, especially with patients in remote areas. Due to a number of factors, further studies are needed to develop tools that can be helpful and cost effective in improving patient’s quality of life. Objective: Our aim is to learn the main reasons palliative patients in Saudi Arabia sought help via calling the free 24/7 hotline and to discuss the hotline’s satisfaction and effectiveness in solving the palliative patient’s concerns during COVID-19. Methods: A cross-sectional sample analysis was obtained from 214 patients from different regions in Saudi Arabia. A total number of 843 calls were made to the 24/7 PC hotlines from the period of 17 April 2020 to 28 February 2021, shortly after COVID-19 pandemic began. The purpose of the call, the caller's relationship to the patient, the status of the complaint, and the satisfaction rate were collected at the end of the call through a voluntary phone survey. Results: The primary reasons that palliative patients called the hotline were: 30% for medication refills, (n=247), 24.7% for medical complaints, (n=205), 15.8% were for booking a new appointment (n=131). Patients themselves accounted for 27.8% of the callers and patient’s sons/daughters accounted for 51.3%. 85% of patients said that their issue had been resolved by the end of the call and 89% of our sample were happy with the service provided through the hotline. Conclusion: The 24/7 hotline service for PC patients in Saudi Arabia was successful in its application and resulted in a high level of satisfaction among a wide sample of participants. The main reasons palliative patients reached out were to request medication refills, seek assistance with a medical complaint, and to book a new appointment. Our hotline service effectively solved 85% of patients' issues.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".