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Record W2995006664 · doi:10.1093/bjsw/bcz145

Social Workers under the Spotlight: An Analysis of Fitness to Practise Referrals to the Regulatory Body in England, 2014–2016

2019· article· en· W2995006664 on OpenAlexaff
Sarah Banks, Magdalena Zasada, Robert Jago, Ann Gallagher, Zubin Austin, Anna van der Gaag

Bibliographic record

VenueThe British Journal of Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMisconductSocial workAusterityPublic relationsManagerialismMedicinePsychologyPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0040.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.357
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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