Social work undergraduates students and COVID-19 experiences in Nigeria
Bibliographic record
Abstract
Following the highly contagious nature of the coronavirus disease and the increase in confirmed cases, the Nigerian government, imposed lockdowns, quarantines, and various social distancing measures to curb the rate of infection. Schools were closed, and examinations were postponed indefinitely. Students of private schools were able to resume academic activities online. However, most public schools could not do so, due to lack of infrastructure. This study aimed to qualitatively investigate the impacts of the novel coronavirus on final-year students of social work, at the University of Nigeria. Data was collected from 20 undergraduates using in-depth interviews. Findings showed that the pandemic had negative effects on different aspects of the students' lives. It was also revealed that some of the students were resilient and were able to use various coping strategies to avoid being overwhelmed by the situation. A policy implication of this study is the need for revitalization of Nigerian public universities, as the continued lockdown of schools shows how public universities are poorly managed in the country. This poor management of public schools has made it impossible for a switch to virtual learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".