Knowledge, awareness, and practices of telehealth: A cross-sectional study on psychiatric nurses in Jeddah City
Bibliographic record
Abstract
The emergence of telehealth stands at the forefront of healthcare evolution, particularly in mental healthcare delivery. The efficacy and adoption of this modality, however, are largely contingent upon the awareness and competence of professionals in the field. This study sought to investigate the awareness, attitudes, and proficiency of psychiatric nurses in Jeddah City regarding telehealth, providing insights into its applicability and potential challenges in the region. Findings indicated that a significant 81% of psychiatric nurses in Jeddah City are familiar with telehealth. Attitudinally, the majority viewed telehealth favorably, with an overall mean attitude score of 3.7 ± 0.91 on a 5-point scale. Proficiency-wise, foundational digital skills were robust, with 72.4% showcasing medium to professional competence in basic computing tasks. However, more specialized telehealth-specific tasks identified areas for enhancement, such as installing software where only 40.4% demonstrated professional or medium competency. Psychiatric nurses in Jeddah exhibit a strong foundational readiness for the integration of telehealth, underscored by their considerable awareness and largely positive attitudes. Targeted training, especially in niche digital areas, is paramount to ensure telehealth's seamless integration and efficacy in mental healthcare delivery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".