Assessing Student Perceptions of a Norwegian University's COVID-19 Response Strategy: A Cross-Sectional Study
Bibliographic record
Abstract
Aim: This study aims to investigate Norwegian students' perceptions toward a higher education institution (HEI)'s COVID-19 response strategy, differentiating between three behavioral techniques: informing (i. e., email updates about COVID-19), nudging (i.e., visual cues as reminders), and creating novel opportunities (i.e., provision of antibacterial dispensers). In addition, the study assesses to what extent these perceptions are influenced by COVID-19 related psychological factors: risk perception; attitudes toward infection prevention and control (IPC) behaviors; perceived behavior control; institutional trust. Methods: A cross-sectional online survey was conducted among a student population. The survey was developed to evaluate the HEI's response strategy, and distinct perceptions of COVID-19 and related practices. Structural equation modeling (SEM) was applied to estimate the effect of the psychological factors on the attitude toward different behavioral techniques. Results: Creating novel opportunities was perceived most positively from the students, secondly, informing the students through email updates about COVID-19, finally, reminders through visual cues. Institutional trust presented the largest positive effect on informing the students through email updates, while no effect was measured for reminders. Attitudes toward IPC behaviors showed the strongest effect on students' perceptions of new opportunities and reminders, whereas providing email updates about COVID-19 is less affected by pre-existing perceptions. Conclusions: A host of factors such as institutional trust, and perceptions concerning IPC measures and risk severity, influence students' perceptions of different behavior change techniques. This type of knowledge can contribute to understanding how perceptions can impact acceptance and adoption of specific preventive measures within a pandemic response. An assessment as such may result in more ethical and relevant future efforts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".