Knowledge, attitude, practices and perceived job stress among physical therapists in the Kingdom of Saudi Arabia during the COVID-19 pandemic: a cross-sectional study.
Bibliographic record
Abstract
AIM: The objective of this study was to assess the level of knowledge, attitude and practice (KAP) and perceived job stress among physical therapists (PTs) during the COVID-19 pandemic in the Kingdom of Saudi Arabia (KSA). METHODS: A cross-sectional study design was adopted; 300 PTs working within the KSA were randomly selected, and the KAP questionnaire was distributed through email using a Google form during the first quarter of 2022. The questionnaire consisted of demographic information, KAP, and perceived stress level at the job. Data analysis was carried out using SPSS 20.0. RESULTS: Most PTs are knowledgeable about the management of COVID-19 patients, where their overall correct response to the items of the knowledge-related questionnaire was 87%. Most PTs had positive attitudes toward successful control of COVID-19 (83%) and took necessary precautions, such as frequent handwashing (97.2%) and adherence to the Centers for Disease Control & Prevention (CDC) guidelines (91.5%) during practice. The overall job stress level of the PTs was 'Moderate' (76.5%). This study showed a significant association between the level of job stress experienced by the PTs and selected demographic variables. CONCLUSION: PTs have adequate knowledge, exhibit a positive attitude and adhere to CDC guidelines while managing patients during the COVID-19 pandemic. Most PTs are prone to moderate job stress while managing patients during the COVID-19 pandemic, and appropriate strategies must be devised to alleviate their job stress and improve their efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".