Investigation of Play Intervention for Dementia (PID) Activities in Addressing Cognitive Domains Reflected in Hong Kong Montreal Cognitive Assessment (HK-MoCA)
Bibliographic record
Abstract
Background/Objectives: Yee Hong Play Intervention for Dementia (PID) is a community program strengthening East Asians >65 years with dementia in their daily functional activities. We analyzed how PID activities align with Hong Kong Montreal Cognitive Assessment. Methods: Utilizing observation sheets procured from documentation notes from the twice-weekly PID sessions, cognitive domains were identified. Mean time duration and activity frequencies were compared between high and low competency client groups. Results: Independent of competency group, activities predominantly targeted attention/concentration (23.8% HC, 16.4% LC), and hand-eye coordination (19.1% HC, 28.7% LC). Less focused domains were delayed recall (3.1–4.7%) and naming (1.3–1.5%). Conclusions: Yee Hong PID tested innovative cognitive domains not currently covered in HK-MoCA screening assessment, emphasizing attention/concentration-oriented activities and none assessing orientation and language domains. Additionally, presence of new domains such as hand-eye coordination and fine motor dexterity suggested that strict adherence with standardized screening tools (e.g., MoCA) may not be ideal. Likely, facilitators have developed innovative measures to assess individual competency to strengthen resilience in our geriatric population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".