The Role of Online Psychotherapy in COVID-19: An Evidence Based Clinical Review.
Bibliographic record
Abstract
BACKGROUND: COVID-19 is an infectious disease that is easily widespread and has clinical manifestations as mild, moderate, or severe disease. COVID-19 patients are required to be isolated during treatment to reduce transmission. This can cause anxiety and depression, which in turn worsens the patient's illness. Providing supportive psychotherapy can help provide a feeling of safety, comfort and calm for patients. The choice of method in providing supportive psychotherapy can be done online/teleconsultation or internet-based. This clinical review aims to determine the effect of online teleconsultation or internet-based psychotherapy on COVID-19 patients. METHODS: A systematic search was performed using online databases, such as PubMed, Cochrane, EBSCO/CINAHL and ProQuest. The identified articles were screened using eligibility criteria. There were 2 studies (Zhou et al, and Wei et al) which were analyzed critically using the Newcastle Ottawa Scale. RESULTS: Both studies showed that management of psychotherapy through teleconsultation or internet-based on COVID-19 patients can help relieve the patient's psychological symptoms. Zhou et al studied 63 suspected COVID-19 with 23.8% (n = 15) having a Hospital Anxiety Depression Scale (HADS) score of 8 or more. There was a significant decrease in HADS-A nxiety (HADS-A) scores (p <0.01) and HADS-Depression (HADS-D) scores (p <0.01) in 15 patients, and from the overall HADS scores (p < 0.01). Wei et al. Showed 17-HAMD and HAMA scores in the online psychological intervention group also showed a significant reduction in symptoms of depression and anxiety compared to controls. CONCLUSION: Psychotherapy through online teleconsultation or internet-based on COVID-19 patients can help relieve symptoms of anxiety and depression and teleconsultation and also effective in dealing with psychological complications in patients with COVID-19.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".