TOTAL e-Learning: An online educational experience to support care partners of people living with dementia in Quebec
Bibliographic record
Abstract
Context: Caregivers of People Living with Dementia (PLWD) face multiple challenges in their caregiving role where the informal care provided by the caregivers of PLWD in Canada has a financial burden on the total out-of-pocket costs of $1.4 billion. In addition, it takes 1.5 times more informal care time for caregivers of PLWD than other informal caregivers. Furthermore, these caregivers do not have the resources that can educate them on their caregiving journey which adds to their challenges. Objective: Optimize and deliver ten online modules over ten weeks for adult learners (TOTAL e-Learning) that aim to support and educate the caregivers of PLWD in Quebec. Design and Analysis: Convergent parallel mixed-methods study with an overarching community-based participatory research (CBPR) methodology. The content and delivery of the online modules was carried out with subject matter experts and caregivers of PLWD (CBPR). Quantitative phase: questionnaires were analyzed using descriptive statistics. Qualitative phase: Observational data, post-module open-ended questions and focus groups where data was analyzed thematically. Setting: Online educational program including online modules and synchronous support sessions. Synchronous sessions and focus groups carried out on Zoom. Population Studied: Caregiver of PLWD in Quebec Intervention/Instrument: Ten online modules Outcome Measures: Quant: descriptive statistics of participants and counts and percentages describing the categorical variables. Qual: major themes related to participation in blended learning program and content. Results: 30 participants joined this pilot study, 63% completed either half or more of the program. Quant: 79% reported the program to be very relevant to their situation, 86% understood it very well, 86% found it taught them something new, 79% found it allowed them to validate their actions and refreshes their memory, 100% reported that they will be using the information learnt, and 86% will use the information to help them take actions or to better understand their situation. Qual: We were able to identify the major themes related to the participation in an online blended learning experience including the emotional load and the psychological benefits of the experience. Conclusions: The results of this work contribute to the implementation of innovative approaches to community education considering inclusiveness and accessibility
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".