Education Program for Enhancing Health Care Students’ Attitudes Toward People Living With Dementia: Protocol for a Single-Arm Pre-Post Study
Bibliographic record
Abstract
BACKGROUND: Health care students are instrumental in shaping the future of dementia care. Cultivating a positive attitude and understanding toward people living with dementia is crucial for diminishing the stigma associated with the condition, providing effective and person-centered care, and enhancing the quality of life for people living with dementia. Educational programs about dementia are increasingly recognizing the potential of gaming tools. OBJECTIVE: This study aimed to evaluate the effectiveness of gaming-based dementia educational programs in improving attitudes toward people living with dementia among health care students. METHODS: This single-arm pre-post study will be conducted among health care students in Indonesian universities. This educational program based on gaming tools will consist of a lecture on dementia, the use of N-impro (gaming tool), and the enactment of short dramas depicting desirable and undesirable communication with people living with dementia behaviors. We will assess attitudes toward people living with dementia, intention to help people living with dementia, knowledge of dementia, and the stigma associated with people living with dementia. The gaming-based dementia education program will be integrated into the curriculum of the health care students. The program will be implemented once with a duration of 90 minutes. RESULTS: Data collection will occur from August 2023 to March 2024. Analysis of the data will be finalized by May 2024, and the outcome will be determined by July 2024. The impact of the gaming-based dementia educational program on improving attitudes toward people living with dementia will be reported. The study findings will be published in a peer-reviewed journal. CONCLUSIONS: The gaming education program demonstrates significant potential in enhancing attitudes toward people living with dementia across various countries, introducing an innovative method for the community-based support of people living with dementia. TRIAL REGISTRATION: ClinicalTrials.gov NCT06122623; https://clinicaltrials.gov/study/NCT06122623. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/62654.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".