How self directed support is failing to deliver personal budgets and personalisation
Bibliographic record
Abstract
Over the past five years, social care has been experiencing a period of change described as 'transformational'. It has largely been based on a model initiated by the organisation 'In Control' (Pol et al., 2006), variously called 'personalisation', 'personal budgets' and 'self directed support'. The drive to create personalised services through self directed support and personal budgets was implemented before the model was fully tested. Indeed, its implementation was announced before completion of a national evaluation set up by the Government. One advantage of such speedy, widespread implementation is that we are now beginning to have substantial evidence regarding its efficacy. At the same time, we are on the cusp of new legislation likely to shape social care for the foreseeable future. It is essential that legislation takes on board what the evidence says about this model - its strengths and weaknesses. The following discussion shows why the underpinning notion of self-directed support seems to have failed in its ambitions. However, the concepts of personalisation and personal budgets associated with it may retain value if interpreted in an appropriate way, delivered through an appropriate strategy. Then even so long as resources fall short of needs, they are likely to ensure the best possible outcomes for service users are secured. If and when adequate levels of funding are also provided, there may be the real prospect of enabling all to live their lives on the same terms as others who do not need social care support.
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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 | 0.040 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".