Psychosocial Impact of COVID-19 Tests and Positive Results on Clinical Students Screened during the Second wave of COVID-19 Pandemic in Rivers State, Nigeria
Bibliographic record
Abstract
Background: Destabilization of all educational activities, mixed impact on academic research and professional development, severe effect on the educational assessment system, and reduced employment opportunities, were some of the impacts recorded in the educational sector following the COVID-19 pandemic. The aim of this study was to evaluate the psychosocial impact of COVD-19 and COVID-19 test and results on returning clinical students in a Private Medical University in Port Harcourt in the first quarter of year 2021. Materials and Methods: A descriptive cross-sectional study was conducted among returning clinical students in a private medical university. Semi-structured questionnaire was used to obtain data from clinical students. Data was analysed using the IBM Statistical Package for Social Sciences (SPSS) version 20.0. Results: A total of seventy-six (76) respondents who were medical students were involved in the study, with a 98.0% response rate. The impact of the pandemic was felt at home and in school. Study found that 49 students (64.5%) were dissatisfied with school work and twenty-two respondents (28.9%) were observed to be highly stressed while 9 students (11.8%) were observed to have no stress. The female respondents were more affected by the COVID-19 pandemic than their male colleagues. Conclusion: Assessment of the impact of COVID-19 disease and testing revealed that undergraduate clinical students were affected psychosocially in the pandemic. Respondents were observed to be highly stressed, and a few indicated incapacitating psychological distress. Therefore, psychosocial support of students should be built into response measures for future screening services.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".