I FELT GUILTY [THAT] I DIDN’T DO ENOUGH. ORGANIZATIONAL AND POLICY RESPONSES EXACERBATED FRONTLINE SOCIAL WORKER DISTRESS
Bibliographic record
Abstract
This study explores urban social workers’ experiences working the front lines during COVID-19’s first wave. It aims to uncover social workers’ shifts in roles and responsibilities across the health and social service network, to illuminate how these shifts impacted them, and ultimately to derive meaning from these experiences to inform future directions for the profession. Eight social workers from a range of contexts were interviewed. Our analyses revealed that, while all participants described some negatives of front-line pandemic work, the frequency and intensity of these moments were exacerbated by organizational and policy responses. When social workers were expected to work outside of their scope of practice, when their skills were overlooked or underutilized, and when their organizational contexts focused on individual distress rather than collective support, they reported intensified periods of distress. If we hope to retain the health and wellbeing of our workforce and preserve the value of the profession, systemic preventative responses must take priority. Building opportunities for collective on-going peer support and debriefing, leveraging the expertise of social workers to address psychosocial issues, and including the voices of front-line workers in the development of solutions to pandemic-related hardships may help reduce social work distress and improve front-line workers’ responses to social issues.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".