Bibliographic record
Abstract
Long-term conditions including cardio-metabolic, severe mental illness such as bipolar disorder and neurological disorders carry the risk of poor wellbeing, social isolation and distress. Associated with these problems are poor care and self-care, resulting in poor health outcomes and shortened life expectancy. The symposium will bring together researchers from the UK , Canada and Pakistan with the aim to highlight some of the challenges which people with serious long term conditions experience and the factors which impact on the wellbeing of these groups: integration of service provision for people with dementia, the role of social support in relation to adherence in adults with type 2 diabetes, and in adolescents with type 1 diabetes, the role of personality factors and stress on quality of life in diabetes, and finally the role of a simple training programme in improving services for people with severe mental illness. The latter is a much-neglected group with a life expectancy frequently estimated at 20 years shorter than that of people without severe mental illness. The symposium will provide a practical overview on the range of factors and interventions relevant to wellbeing and medical outcomes for a wide range of serious long term conditions, in a range of cultural contexts.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".