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Record W1522381567

Supporting People With Long-term Conditions

2014· article· en· W1522381567 on OpenAlexaboutno aff
Jörg Huber

Bibliographic record

VenueEuropean Health Psychologist · 2014
Typearticle
Languageen
FieldPsychology
TopicFamily Caregiving in Mental Illness
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyDistressPsychological interventionDementiaQuality of life (healthcare)Mental healthSocial isolationPsychologyMental illnessPersonalityGerontologyPsychiatryType 2 diabetesMedicineClinical psychologyDiabetes mellitusSocial psychologyDiseasePsychotherapistEnvironmental healthPopulation
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.038
GPT teacher head0.402
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2014
Admission routes1
Has abstractyes

Explore more

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