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Record W2569986521 · doi:10.1108/tldr-03-2016-0008

Implementing policy and good practice in services for people with learning disabilities: factors influencing commissioning and service provision

2017· article· en· W2569986521 on OpenAlexaboutno aff
James Colman Kerrigan, Caroline Hopper

Bibliographic record

VenueTizard Learning Disability Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsnot available
Fundersnot available
KeywordsEnthusiasmProject commissioningOriginalityLearning disabilityRelevance (law)Value (mathematics)Service (business)Challenging behaviourPublishingPublic relationsAged careMental healthMainstreamPsychologyGood practiceMedicineNursingPolitical scienceMarketingPsychiatryBusinessSocial psychologyEngineering

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to explore the implementation of learning disability (LD) policy among LD commissioners and managers in Kent (South East England) and a neighbouring area. Design/methodology/approach Participants’ views were elicited by semi-structured interviews focussed on two key national policy documents: Valuing People (DH, 2001) and the Mansell report (DH, 1993; 2007a). Findings Valuing People had a significant impact at the time of publication but initial enthusiasm and impetus faded over time. The Mansell report was thought to have had little impact on local services. Good progress was reported with respect to the development of more integrated services. Limited progress was identified with respect to the development of local mental health and challenging behaviour services. Factors influencing policy implementation were identified. Originality/value The similarity of findings to those of McGill et al. (2010) suggest their more general relevance. In the light of the subsequent investigation into Winterbourne View, common themes from both studies are considered in relation to the current Transforming Care programme in England.

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)0.005
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.677
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.427
Teacher spread0.374 · 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 designObservational
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

Citations3
Published2017
Admission routes1
Has abstractyes

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