Motivations of patients and their care partners for visiting a memory clinic. A qualitative study
Bibliographic record
Abstract
OBJECTIVE: We investigated motivations of patients and care partners for their memory clinic visit, and whether these are expressed in consultations. METHODS: We included data from 115 patients (age 71 ± 11, 49% Female) and their care partners (N = 93), who completed questionnaires after their first consultation with a clinician. Audio-recordings of these consultations were available from 105 patients. Motivations for visiting the clinic were content-coded as reported by patients in the questionnaire, and expressed by patients and care partners in consultations. RESULTS: Most patients reported seeking a cause for symptoms (61%) or to confirm/exclude a (dementia) diagnosis (16%), yet 19% reported another motivation: (more) information, care access, or treatment/advice. In the first consultation, about half of patients (52%) and care partners (62%) did not express their motivation(s). When both expressed a motivation, these differed in about half of dyads. A quarter of patients (23%) expressed a different/complementary motivation in the consultation, then reported in the questionnaire. CONCLUSION: Motivations for visiting a memory clinic can be specific and multifaceted, yet are often not addressed during consultations. PRACTICE IMPLICATIONS: We should encourage clinicians, patients, and care partners to talk about motivations for visiting the memory clinic, as a starting point to personalize (diagnostic) care.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".