Development of Moving Pictures GENIE‐an online gateway to dementia resources to support Culturally and Linguistically Diverse (CALD) communities
Bibliographic record
Abstract
Abstract Background Over the past decades the use of technology in healthcare has increased exponentially, and the recent pandemic further accelerated the dependence on technology, both to access health and care services but also digital resources and information for health care consumers. Carers of people living with dementia from culturally and linguistically diverse (CALD) backgrounds have been disproportionately impacted as in‐language resources are scarce and locating them can be difficult. Thus, there is need to centralize digital resources to improve access for carers of persons living with dementia who speak languages other than English. Responding to this, our project co‐produced the GENIE (Global dEmeNtIa rEsources): a comprehensive, global online gateway to culturally and linguistically appropriate digital dementia resources. Method Methods comprised an environmental scan (academic literature and systematic online search) and online consultations with key stakeholders to identify resources. Resources were included if developed by healthcare organizations, dementia or aged care peak bodies, dementia advocacy groups, government departments, or academic organizations. The GENIE site was coproduced with CALD carers and providers in Australia. Data analytics are being collected via the website. Result The environmental scan highlighted the dearth of resources developed for CALD communities, and gaps in availability of digital dementia resources across many languages. Further, dementia resources identified via the literature search were often not available for public access, required payment, or were no longer available. More than 400 resources in 68 languages were identified and included in the gateway, and the site was made publicly available in November 2022. Data analytics show that the uptake of the resource has been good with > 700 new users and >3500 page views in the first two months. Conclusion The Moving Pictures Genie project has produced one of the most comprehensive gateways to digital dementia resources to support carers and families who speak languages other than English currently available. In addition to the production of the resource, the project provides insights into the gaps in existing resources across languages and geographical regions. These findings will be useful to guide the direction of resource development to avoid duplication and enhance equity and benefit to CALD communities.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".