I FELT GUILTY [THAT] I DIDN’T DO ENOUGH. ORGANIZATIONAL AND POLICY RESPONSES EXACERBATED FRONTLINE SOCIAL WORKER DISTRESS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.011 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 it