A Case Study of Social Work Leadership in the Pandemic Intervention in Wuhan
Bibliographic record
Abstract
Abstract Social workers in Wuhan, China were among the first to respond to the public health crisis caused by Coronavirus disease (COVID-19) in early 2020. Social workers in Wuhan developed and implemented an effective interventional model integrating online and offline volunteers of multiple professions—the ‘4 + 1’ model—to support affected individuals in the process of battling the pandemic. Transformational social work leadership played a vital role in the widely adopted model in China, characterised by idealised influence—attributed (or charisma); idealised influence—behavioural; inspirational motivation; intellectual stimulation and individual consideration. Contextual performance is also discussed, followed by a discussion on why social work can play a leadership role in inter-disciplinary intervention in the pandemic crisis. The article concludes with the areas social workers can improve on for the betterment of leadership.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".