Bibliographic record
Abstract
INTRODUCTION: Individuals with early-onset Alzheimer's disease face many challenges and barriers older adults with late-onset Alzheimer's do not. Unfortunately, information about early-onset Alzheimer's disease is in its infancy stage in comparison with late-onset Alzheimer's. PURPOSE/AIMS: The purpose of this study was to examine the lived experiences of a 54-year-old man with early-onset Alzheimer's disease and his family (wife, sister, and mother) to understand the effects on the family unit. DESIGN: Interpretive phenomenology was used to guide this study. METHODS: All participants completed 2 in-person one-on-one interviews, and a final interview was completed online. Field notes, member checks, and triangulation were used to enhance the study's credibility. RESULTS: This article focuses on the theme "'A big curve ball': Disruption of the life cycle." Participants indicated the major financial and social challenges experienced by Joe and his wife. Furthermore, participants emphasized the importance of acceptance and maintaining a positive attitude to help cope with Joe's diagnosis. CONCLUSIONS: The accounts of Joe and his family shed light on an area relatively void in the literature. In addition, Joe's experiences may provide comfort for other families facing early-onset Alzheimer's disease. The implications for community health nurses in assisting individuals with early-onset Alzheimer's disease and their families are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".