Factors influencing the physio psychosocial state and health concerns of Canadian and Iranian undergraduate nursing students during the pandemic
Bibliographic record
Abstract
Abstract The aim of our study is to investigate and analyze the factors that influence the physical, psychological, and social well-being of nursing students in Canada and Iran amidst the COVID-19 pandemic.The current study is both descriptive and comparative. Nursing students from both Canadian and Iranian institutions. Data was collected using a questionnaire based on the Short Health Anxiety Inventory (SHAI) scale and the Physio-Psychosocial Response Scale (PPSRS). There were statistically significant positive and extremely strong correlations (respectively; r=.911, r=.964, r=.952). between the total score of the physio-psycho-social response scale and the social, emotional, and somatic subscales of the students' subgroups. According to our findings, the COVID-19 pandemic has had a negative impact on the physio-psycho-social well-being of Canadian and Iranian nursing students. The total scores of the physio psychosocial response scale and the health anxiety scale were found to have a statistically significant positive relationship.The research findings support that during the pandemic, psychological support initiatives for nursing students should be prioritized. Moreover, the present study could be valuable in assessing the immediate psychological needs of the general population who are encountering physical symptoms during the epidemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".