What Is Our Added Value? A Systematic Analysis of Epidemic Narratives in the Social Work Literature
Bibliographic record
Abstract
Abstract This article addresses social work’s singular conceptual and analytical contribution to the field of epidemics. A systematic literature review was conducted to analyze how social work studies overlap to construct epidemic narratives. The author collected 601 articles from the Social Services Abstracts database and carried out a targeted search within 20 social work journals. Five epidemic narratives were identified: (1) a psychosocial consequences narrative, (2) a social work competence narrative, (3) a social risk factors narrative, (4) a misinformation narrative, and (5) a power matrix narrative. Results highlighted the social success of psychosocial perspectives prevalent in classic public health narratives. This understanding relies on a “politics of access” perspective and advocates for the improvement of current social services. The findings revealed that social work does not have a conceptual specificity in the field of epidemics but, rather, its current distinctive contribution mostly lies in its use of social work–centric inquiries that analyze social work practices and describe the consequences experienced by social work actors during epidemics. The author argued that the social work literature could benefit from analyses informed by a “politic of emancipation” that are less prominent in the analyzed studies. Avenues for future research are considered.
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 | 0.079 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.045 | 0.028 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".