TECHNOLOGY PERCEPTIONS AMONG CHINESE FAMILY CAREGIVERS OF PERSONS WITH DEMENTIA: A SEX-GENDER LENS
Bibliographic record
Abstract
Background: Dementia is a major public health concern associated with significant caregiver demands. While technologies are available to assist with caregiving, there is a paucity of information on caregiver needs and preferences for these technologies, especially among Chinese family caregivers of persons with dementia. Objective: The purpose of this study was to examine the technology needs and preferences of Chinese family caregivers of persons with dementia in Canada. Methods: A cross-sectional pilot survey was conducted through the Yee Hong Centre of Geriatric Care in Ontario, Canada in English and Mandarin. Respondents could complete the questionnaire over the phone, internet, or by mail. Frequency distributions, Wilcoxon Signed Ranks Test and multiple regression analyses were used to examine difference by sex/gender.. Results: The majority of the 40 respondents to date did not demonstrate knowledge about technology that can assist with caregiving. Ease of installation and reliability were identified as the most important features when installing and using technology. Despite little knowledge about technology, respondents demonstrated a positive attitude towards technology use during caregiving. Controlling for age, female respondents were significantly more receptive to technology compared to males. Conclusions: Our findings suggest a need to increase awareness of technology options to assist with caregiving in this population. They provide insights for future development and marketing of technologies that better align with the needs and preferences of male and female caregivers. Further exploration of additional environmental and social influences on technology perception is warranted to better understand and tailor technologies for this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".