Knowledge, attitudes, and stigma relating to rarer dementias among members of the general public in an international cohort
Bibliographic record
Abstract
Part one presents a conceptual introduction reviewing the literature on \ndementia related knowledge, attitudes, and stigma among the general public, and \ndiscusses the implications for the less common forms of dementia. \nThis thesis is a study within the studies of the Rare Dementia Support (RDS) \nImpact study: a 5-year programme of research exploring the impact of \nmulticomponent support groups for those living with rare dementias. It is a \ncollaboration between University College London (UCL), Bangor University and \nNipissing University in Canada (http://www.raredementiasupport.org/research/) and \nis joint funded by the Economic & Social Research Council (ESRC) and National \nInstitute for Health Research (NIHR) and ethical approval for the study was granted \nby UCL Ethics Committee (Reference: Project ID: 8545/004). The presented thesis \nis my own work, supervised by Dr. Joshua Scott Yes. I was involved in the design of \nthe study, completed the data collection and analysis independently with exception \nfor the following contributors: \n• Emilie Brotherhood involved in the ethical approval amendment and \napplications for this thesis. \n• Joanna Stroud (Head of Online Learning at UCL) who set the study’ surveys \nup on Future Learn the open education platform which houses The Many \nFaces of Dementia Massive Open Online Course. Joanna also linked the \nSurveys to Qualtrics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".