Psychological Impacts of COVID-19 in Dental Patients are Moderated and Mediated by Hospital-Infection-Control-Policy and Satisfaction-with-Life: A Prospective Observational Dental-COVID Study
Bibliographic record
Abstract
Introduction: COVID-19 pandemic has imposed nation-wide lock-downs which severely impacted day-to-day lifestyle and caused anxiety, stress and fear among patients taking medical care including dental treatments. These psychological behaviors have also been observed during the strategic relaxation of social restrictions (Unlock). However, potential effect of these psychological behavior in endodontic cases have not been probed. Here, it is of great interest to explore the magnitude and buffering effect of two important psychological resources: satisfaction-with-life, and confidence in hospital-infection-control-policy in relation with COVID-19 risk perception on psychological impact due to fear for COVID-19. Methods: Patients visiting Dental Clinic & Research Center for endodontic procedures were randomly asked to fill survey questionnaire, and were later enrolled as per the study criteria. The study carried out in two phases: from 15 Sept 2020 to 15 Dec 2020 (restrained confinement; Unlock 1.0); and from 16 Dec 2020 to 12 Feb 2021 (mild confinement; Unlock 2.0) with total sample size of 136. We used data collection tools such as fear-of-COVID-19 scale (FCV-19), perceived-stress-scale (PSS), modified-dental-anxiety-scale (MDAS), satisfaction-with-life scale (SWLS), COVID-19 risk perception, and confidence in hospital-infection-control-policy for COVID-19. Results: A double moderation and dual moderated mediation structured model were used to establish the correlation of various parameters using SPSS (version 25.0) software suite. Confidence in hospital-infection-control-policy and SWLS were negatively correlated with FCV-19, MDAS, and PSS. Risk perception of COVID-19 was found to positively associated with FCV-19, MDAS, and PSS. Discussion: Patient's confidence in hospital-infection-control-policy for COVID-19 and SWLS acted as independent moderator for FCV-19 and mental distress. FCV-19 and risk perception were found to be lower during mild confinement (Unlock 2.0), and were also the positive predictors of PSS; and negative predictors of SWLS. The higher SWLS correlated very well with lower COVID-19 risk perception, concerning PSS and MDAS.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".