Interdisciplinary and Transdisciplinary Perspectives: On the Road to a Holistic Approach to Dementia Prevention and Care
Bibliographic record
Abstract
Dementia, of which the most frequent form is Alzheimer's disease, is a chronic and terminal condition with multi-factorial causes and numerous consequences on a patient's life. Combining perspectives from different disciplines seems necessary for unraveling dementia's entangled issues. Current dementia management is a multidisciplinary effort; however, integrating different disciplines as a holistic treatment process is often hindered due to different responsibilities, various conceptual approaches, and distinctive research methods. With this paper, we raise some of the challenges that need to be addressed in order to initiate an interdisciplinary or even transdisciplinary research agenda. We also outline recommendations on how to integrate multiple disciplinary perspectives in dementia care and research. We see opportunities for young investigators to draw from different fields of research in dementia as their research focus is still developing. By establishing common objectives with investigators from other fields, we can pursue the goal of improving treatment and care as a team-meaning accomplishing different tasks but sharing a common purpose. It is necessary to address the communication between fields that limits the understanding of connections between cognitive symptoms, biological processes, treatment, lifestyle, and care giving in order to reach the aim of developing a holistic, person-centered, patient-first approach. Associating biomedical research to field experience from care professionals and the study of human science will promote a more independent, social, and sustainable lifestyle for people with dementia.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".