Mental Health Support for COVID-19; A Retrospective Report on the Psychosocial Response Strategy in Lagos, Nigeria
Bibliographic record
Abstract
Abstract Background : The COVID-19 pandemic has been described to have significant effects on the mental health and emotional well-being of people all over the world generally. This study aimed at describing the set up and functions of the psychosocial support service for the COVID-19 response in Lagos South West Nigeria. Methods: A retrospective and descriptive overview of the psychosocial support for covid-19 patients supported in Lagos, Nigeria over a four-month period. Remote interventions given included Psychological first aid, counselling, psycho pharmacology, and virtual support group sessions. Results : The Psychosocial care provided was a part of the Covid-19 outbreak response strategy in Lagos State, Nigeria. Over 43,000 support calls were made to Covid-19 positive persons; 1,316 hospitalised patients were supported remotely during isolation and more than half declined admission, opting to self-isolate at home instead. Psychosocial home care support group sessions and discharge support groups held. Liaison with all other thematic pillars of the response was key. Conclusion : A comprehensive multidisciplinary mental health and psychosocial support service is an integral part of the management strategy during the pandemic response. The Psychosocial support by the Lagos Covid-19 response presents a practical reproducible approach.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".