Evaluation of ChatGPT’s responses to information needs and information seeking of dementia patients
Bibliographic record
Abstract
Abstract Background: Many people in the advanced stages of dementia require full-time caregivers, most of whom are family members who provide informal (non-specialized) care. It is important to provide these caregivers with high-quality information to help them understand and manage the symptoms and behaviors of dementia patients. This study aims to investigate the evaluation of ChatGPT, a chatbot built using the GPT large language model,in responding to information needs and information seeking of such informal caregivers. Methods: We identified the information needs of dementia patients based on the relevant literature (22 articles were selected from 2442 retrieved articles). From this analysis, we created a list of 31 items that describe these information needs, and used them to formulate relevant 118 questions. We then asked these questions to ChatGPT and investigated its responses. In the next phase, we asked 15 informal and 15 formal dementia-patient caregivers to analyze and evaluate these ChatGPT responses, using both quantitative (questionnaire) and qualitative (interview) approaches. Findings: In the interviews conducted, informal caregivers were more positive towards the use of ChatGPT to obtain non-specialized information about dementia compared to formal caregivers. However, ChatGPT struggled to provide satisfactory responses to more specialized (clinical) inquiries. In the questionnaire study, informal caregivers gave higher ratings to ChatGPT's responsiveness on the 31 items describing information needs, giving an overall mean score of 3.77 (SD 0.98) out of 5; the mean score among formal caregivers was 3.13 (SD 0.65), indicating that formal caregivers showed less trust to ChatGPT's responses compared to informal caregivers. Conclusion: ChatGPT’s responses to non-clinical information needs related to dementia patients were generally satisfactory at this stage. As this tool is still under heavy development, it holds promise for providing even higher-quality information in response to information needs, particularly when developed in collaboration with healthcare professionals. Thus, large language models such as ChatGPT can serve as valuable sources of information for informal caregivers, although they may not fully meet the needs of formal caregivers who seek specialized (clinical) answers. Nevertheless, even in its current state, ChatGPT was able to provide responses to some of the clinical questions related to dementia that were asked.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".