Identification of emerging harms due to COVID-19 outbreak: a qualitative study in Iran
Bibliographic record
Abstract
INTRODUCTION: Although COVID-19 has altered various harms and exacerbated the prevalence of some of them, this period has also set the stage for the emergence of new harms. The present study aims to identify the emerging harms resulting from the COVID-19 outbreak in Iran. METHODS: The study was conducted using a qualitative content analysis approach through semi-structured interviews with 21 experts and professors knowledgeable about social harms and COVID-19 consequences who were selected through purposive and theoretical sampling. Data analysis was carried out using the Graneheim and Lundman's method in MAXQDA-2018 software. Guba and Lincoln's criteria were used to trustworthiness of results. RESULTS: The results showed that the COVID-19 pandemic led to a range of issues and problems at various levels of society that were not considered social harms before the pandemic, given their prevalence and impact. After analyzing the data, four main categories and fourteen subcategories were identified. The main categories were social fatigue, ineffective education system, formation of a digital lifestyle, and formation of a new understanding and meaning of death and life. CONCLUSION: The COVID-19 crisis has intensified existing social harms and introduced new ones, rendering previous mitigation strategies ineffective. Designing novel policies and guidelines is crucial to address these evolving challenges and reduce the adverse societal impacts of the pandemic.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".