To what extent does diagnosis matter? Dementia diagnosis, trouble interpretation and caregiving network dynamics
Bibliographic record
Abstract
Abstract Contemporary research into health treats diagnosis as a central step in illness management and trajectories. Most public health policies, especially in the case of Alzheimer’s disease, claim that the earlier a diagnosis is made, the better it is for patients and caregivers. Quantitative and qualitative analysis from our longitudinal interview study, conducted with 60 caregivers of persons diagnosed with dementia, shows that this usual model of diagnosis [symptoms → diagnosis → meaning and caregiving] should be nuanced. First, diagnosis does not follow increased symptoms, but occurs rather through a process involving the observability of patients’ troubles and their interpretation of said troubles as requiring medical assistance ‐the ‘trouble?observability?interpretation convergence.’ Second, diagnosis does not systematically trigger the mobilisation of a caregiving network: such mobilisation may follow the diagnosis, but it can also provoke it, temporarily prevent it, or have no immediate impact. These observations beg the question: To what extent does diagnosis matter? We conclude by questioning the centrality of diagnosis in the illness trajectories and its crucial role in the mobilisation of a caregiving network, that is often taken for granted, and propose to distinguish between ‘anticipation diagnosis’ and ‘emergency diagnosis’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".