Systemic safety inequities for people with learning disabilities: a qualitative integrative analysis of the experiences of English health and social care for people with learning disabilities, their families and carers
Bibliographic record
Abstract
BACKGROUND: Failures in care for people with learning disabilities have been repeatedly highlighted and remain an international issue, exemplified by a disparity in premature death due to poor quality and unsafe care. This needs urgent attention. Therefore, the aim of the study was to understand the care experiences of people with learning disabilities, and explore the potential patient safety issues they, their carers and families raised. METHODS: Two data sources exploring the lived experience of care for people with learning disabilities were synthesised using an integrative approach, and explored using reflexive thematic analysis. This comprised two focus groups with a total of 13 people with learning disabilities and supportive staff, and 377 narratives posted publicly via the feedback platform Care Opinion. RESULTS: The qualitative exploration highlighted three key themes. Firstly, health and social care systems operated with varying levels of rigidity. This contributed to an inability to effectively cater to; complex and individualised care needs, written and verbal communication needs and needs for adequate time and space. Secondly, there were various gaps and traps within systems for this population. This highlighted the importance of care continuity, interoperability and attending to the variation in support provision from professionals. Finally, essential 'dependency work' was reliant upon social capital and fulfilled by paid and unpaid caring roles to divergent extents, however, advocacy provided an additional supportive safety net. CONCLUSIONS: A series of safety inequities have been identified for people with learning disabilities, alongside potential protective buffers. These include; access to social support and advocacy, a malleable system able to accommodate for individualised care and communication needs, adequate staffing levels, sufficient learning disabilities expertise within and between care settings, and the interoperability of safety initiatives. In order to attend to the safety inequities for this population, these factors need to be considered at a policy and organisational level, spanning across health and social care systems. Findings have wide ranging implications for those with learning disabilities, their carers and families and health and social care providers, with the potential for international learning more widely.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".