Access to support groups for people with Alzheimer’s Disease since the COVID‐19 pandemic in the Province of Quebec : an action research approach
Bibliographic record
Abstract
Abstract Background People with Alzheimer’s Disease (PWAD) face reduced social participation and community engagement. Community organizations have developed resources to address these challenges. During the COVID‐19 pandemic, many of them closed, and developed online resources to avoid further increasing the social isolation of PWAD (Armitage and Nellums, 2020). However, there is no consensus regarding their effects and questions remain about the way to implement them (Fields et al., 2020; Xie et al., 2020). Therefore, this study aims to: 1) describe the online support groups implemented by community organizations (e.g., Alzheimer Society) to support PWAD during the COVID‐19 pandemic; 2) Explore the ethical aspects, the safety and respect of privacy of these online groups; 3) Evaluate the engagement of PWAD in relation to these groups and their impact on social participation; 4) Develop methods to expand these resources and make them sustainable. Methods This four‐stage iterative research‐action study is conducted in partnership with the Federation of Quebec Alzheimer Societies (Creswell, 2009; Rauch et al., 2014). Stage 1: involves a qualitative study to identify important factors for the development of online support groups. Stage 2: will consist of co‐creation workshops with our partners to identify areas of improvement for the different support groups offered across the province of Quebec. Following the implementation of these enhanced online support groups. Step 3: will be a sequential mixed‐methods study to evaluate the process of implementing these resources and its effects on the social isolation of PWAD. Step 4: In order to identify ways to expand these resources to other communities, co‐creation workshops will be conducted. Expected results Facilitators and barriers to implement online support groups that respect the safety and privacy of PWAD will be identified. The effects of these online support groups on the social isolation and the engagement of PWAD will be measured to identify best practices for online services. Conclusion The knowledge developed will enable community organizations to expand and refine online resources dedicated to PWAD in the community. Finally, the main social implication of this project is to provide access to safe, ethical and best practices for online support groups for PWAD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".