Implementing policy and good practice in services for people with learning disabilities: factors influencing commissioning and service provision
Bibliographic record
Abstract
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.
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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 | 0.037 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".