‘Being a Social Worker … It's in My DNA’ Retaining Experienced Child and Family Social Workers: The Role of Professional Identity
Bibliographic record
Abstract
ABSTRACT The retention of experienced child and family social workers is a significant issue in the United Kingdom, the United States and Europe. Failure to retain experienced practitioners has serious implications for the protection and support of vulnerable children. Existing research and workforce interventions have focused on the support of early‐career social workers to prevent exit. Relatively few studies have examined what can be learned from experienced social workers who have remained in the profession long‐term. This study captures the voices of experienced stayers. Data consist of interviews with social workers ( n = 58) across 11 local authorities in England who have remained in practice for ≥8 years. Findings suggest that a strong sense of professional identity (PI) sustains social workers and promotes retention. For experienced social workers, staying in the profession long‐term involves navigating a series of identity challenges over the course of their career, conceptualized here as Critical Career Episodes (CCEs). Based on these findings, we suggest that retaining experienced social workers involves support to navigate CCEs alongside meaningful opportunities for learning and development. We identify three key factors that support and sustain ongoing PI development and support retention: generativity, specialism and mobility. The article concludes with recommendations to support workforce retention.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".