Encouraging healthy weight for adults with intellectual disabilities: supporting the role of carers
Bibliographic record
Abstract
Abstract Adults with intellectual (learning) disabilities die on average 20 years earlier than the UK general public, with heart disease a leading cause of avoidable death. Obesity, a key risk factor, is more prevalent in this group. Carers (paid and unpaid) play a vital role in supporting healthy behaviours and healthy weight in adults with intellectual disabilities but often lack appropriate training. This study developed national e-learning for carers based on qualitative research methods. This included 1) content analysis of professional resources to promote healthy weight in people with intellectual disabilities, 2) semi-structured interviews with health and social care managers, clinical leads and commissioners to identify carer barriers and areas of training need, 3) semi-structured interviews and focus groups with paid and unpaid carers to feedback on and refine training content. Thematic analysis of interview and focus group transcripts was undertaken to identify key themes Twenty-one professional resources and 25 participants (across 19 interviews and two focus groups) informed the training. Two overarching concepts explained themes arising from the data - the central yet under-supported role of carers in promoting healthy weight, and the structural and cultural barriers to healthy weight in people with intellectual disabilities. Identified carer training needs included: the health inequalities faced by people with intellectual disabilities, causes and health impacts of obesity, healthy eating and physical activity recommendations, and how to apply these topics when working with an adult with an intellectual disability. The research team worked with an intellectual disability charity ‘experts by experience’ group to develop lived experience videos of individuals’ journeys towards healthier weight included within the e-learning. Carers face many barriers in supporting healthy behaviours in people with intellectual disabilities, including limited knowledge of obesity, nutrition and physical activity. National e-learning was developed from this research, available at: https://www.e-lfh.org.uk/programmes/supporting-healthy-weight-in-an-adult-with-a-learning-disability/ .
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 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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".