Social Workers under the Spotlight: An Analysis of Fitness to Practise Referrals to the Regulatory Body in England, 2014–2016
Bibliographic record
Abstract
Abstract This article examines the nature of, and reasons for, the disproportionately high rates of fitness to practise referrals of social workers in England to the Health and Care Professions Council (HCPC), compared with other professions regulated by HCPC during 2014–2016. In 2014–2015, the rate of referrals for social workers was 1.42 per cent of registrants, compared with an average for the sixteen professions regulated by HCPC of 0.66 per cent. Drawing on published statistics and unique analysis of a sample of 232 case files undertaken as part of a research project in 2016–2017, the article highlights relatively high rates of inappropriate referrals from ‘members of the public’ (mainly service users) particularly in relation to child placements and contact. A detailed picture is offered of the variety of referrals dealt with at each stage of the fitness to practise process (from initial triage to final hearings), with recommendations for how to prevent inappropriate referrals, whilst focusing concern on the most serious cases of incompetence and misconduct. This research is of significance at a time of increasing pressure for social workers, social services and service users under conditions of austerity and managerialism; on-going concerns about standards in social work; and recent changes in social work regulation.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".