Caring4Dementia
Bibliographic record
Abstract
Dementia is a term used when the brain functionality reduces in terms of behaviour, memory and thinking clearly for daily activities. In the early stages, memory impairment limits the memory processes in patients with dementia (PwD). In advanced stages, it affects the PwD's autonomy when performing complex daily activities such as PwD's interaction and communication with people around them. Dementia is becoming one of the major causes of disability and dependency among older people worldwide. It affects the ability of an individual to reason with and to understand others, which creates difficulties in communication between the family caregivers and PwDs. Thus, there is a need for a platform to help family caregivers to communicate with the PwDs efficiently. One of the helpful tools to work with is a mobile application (app). Mobile apps can be widely available and easy to use for the people caring for PwDs. This paper describes the development of a mobile app for people interacting with PwDs. The app contains different scenarios related to daily activities that are usually performed by PwDs. Each scenario includes a set of options for the users and asks them to choose the option in response to the corresponding daily activity. Having chosen the option, the app provides the user with comments which are already included in the app for each scenario. The comments were developed by the research team in partnership with clinicians having more than 5 years of experience with PwDs. Caring4Dementia app can address the communication problem by providing (1) specific knowledge about the PwD's condition, cognitive performance evaluation, and monitoring, and (2) educating on appropriate behaviour to adopt while facing communication challenges associated with dementia. The theoretical framework of a communication training app introduced in the present paper will direct the future empirical investigations where the effectiveness of the app will be compared to the effectiveness of currently existing methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.033 |
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".