Support for decision-making guidance in England: a pragmatic review
Bibliographic record
Abstract
Law and policy concerning personal decision-making increasingly recognizes a role for support to enable greater autonomy and legal recognition for adults whose decision-making ability may be limited. Support for decision making (SFDM) is embedded in England and Wales under the Mental Capacity Act 2005 (MCA). It has also gained traction internationally through the UN Convention on the Rights of Persons with Disabilities (CRPD), to which the UK is a signatory. However, these two legal reference points diverge in their understanding of SFDM, which presents challenges for putting it into practice. A pragmatic review methodology identified 40 resources containing SFDM guidance, providing insight into its implementation and conceptualization in England. An analysis indicates the need for authoritative guidance that provides more multifaceted advice, recognizing key variables including: the nature of the decision, source of decision-making difficulties, and the relationship of the supporter. Gaps in guidance provision are also identified for decision-makers, third parties, and the mental health context. The resources largely conceptualize SFDM as a means to enable mental capacity. However, recent developments propose a CRPD-aligned approach that includes SFDM in the context of substituted decisions. This generates a dualistic model of SFDM in England, raising new questions in this area.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".