Age, Dementia, and Diagnostic Candidacy: Examining the Diagnosis of Young Onset Dementia Using the Candidacy Framework
Bibliographic record
Abstract
People living with young onset dementia face significant barriers to diagnosis, resulting in delays, misdiagnoses, and treatment gaps. We examined the process of accessing and delivering a diagnosis of young onset dementia using the candidacy framework as a conceptual lens. Semi-structured interviews were conducted with six people living with dementia, 14 family members, and 16 providers in a western Canadian city. Participants' accounts revealed the diagnosis of young onset dementia as a negotiated process involving patients, family members, and health professionals. Assumptions about age and dementia affected how participants interpreted their symptoms, how they presented to services, and how they, in turn, were perceived by providers. At the organizational level, age-restrictions, fragmentation, and unclear referral pathways further complicated the diagnostic process. Our findings lend support to the growing call for specialist young onset dementia care and point toward several recommendations to develop more age-inclusive diagnostic services.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".