Understanding the patient voice for medicine development: Qualitative research of the patient journey in Alzheimer's Disease
Bibliographic record
Abstract
Significant disparities exist throughout the patient journey and ultimately in health outcomes across diverse patient populations with Alzheimer's Disease (AD). The aim of this qualitative market research was to better understand the experiences of a diverse group of patients living with AD, to support inclusive and patient-centered approaches in medicine development. Patients diagnosed with mild cognitive impairment (MCI) and dementia due to AD (mild, moderate, or severe), and care partners of patients living with AD, were included in this research. Participants were interviewed one-to-one or as dyads, with follow-up ethnographic tasks. The research included a diverse group of patients across geography, ethnicity, race and employment status. Data was thematically analysed. A total of 65 participants (29 patients and 36 care partners) from the United States (US; 15%), Canada (20%), China (17%), France (12%), Germany (17%), and Italy (18%) were included. The mean patient age was 65.7 years, with the reported stage of disease as MCI or mild dementia due to AD (40%) and moderate or severe dementia due to AD (60%). The analysis revealed an overall consistent patient journey including diagnosis, day-to-day living, care, and treatment experiences. Some variations were revealed in the lived experiences of different patient groups. Many patients from diverse ethnic/racial groups emphasized the importance of family involvement in care and treatment decisions. Black participants in the US expressed concerns with seeking treatment due to lack of representation in medicine development and the need for future medications to be explicitly trialled in participants from ethnic/racial minorities. Asian participants in China, the US and Canada reported major stigma associated with AD, which delayed them in seeking medical help. Across all countries, patients living in rural locations experienced increased financial and logistical burden in accessing healthcare. These findings underscore the need to better understand the unique experiences faced by diverse populations of patients when considering the process for AD medicine development. A patient-centered approach could help increase trust and ultimately improve the patient experiences and health outcomes for diverse populations affected by AD.
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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 | 0.034 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".