The need to adapt after Alzheimer's disease diagnosis: Coping strategies used to maintain identity and quality of life
Bibliographic record
Abstract
Background Receiving a diagnosis of a major neurocognitive disorder due to Alzheimer's disease (AD) brings with it the need to adjust to a new life situation. People with AD seek to (1) maintain emotionally positive goals in their current lives, and (2) use positive experiences from the past to create continuity in their lives, with the aim of maintaining their quality of life and gaining a sense of hope. Objective This research aims to explore the coping strategies and processes used following diagnosis. Method An exploratory qualitative design was implemented to study the different coping strategies used by ten people with AD , via semi-structured interviews. The transcribed data was subject to an interpretative phenomenological analysis. Results All participants experienced unpleasant emotions following their diagnosis. Their coping process following two different trajectories: (1) adaptive coping strategies to gain resilience and hope to maintain meaning in their current lives; (2) less adaptive coping strategies essentially resulting in the denial of the diagnosis and withdrawal from social life. Conclusions This research makes it possible to identify possible intervention paths adapted to an individual's needs to help them move towards adaptive coping strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".