Bibliographic record
Abstract
Informal caregivers (CGs) of people with dementia (PwD) in Ontario may provide upwards \nof 90 hours or more of caregiving (CG) or assistance to a loved one, per week. CGs of PwD \noften face increased social isolation, disrupted routines, and experience adverse health \neffects as this work is incredibly difficult and overwhelming, requiring knowledge, \neducation, resources, and support. eHealth interventions can help to respond to the dynamic \nand changing needs of these CGs. To respond to these needs, Dr. Richard Sztramko \nconceptualized iGeriCare, an educational multimedia tool. 10 iGeriCare lessons were \ncreated and developed by Dr. Sztramko and Dr. Anthony J. Levinson and his team at the \nDivision of e-Learning and Innovation. \nThe objective of this thesis is to review psychoeducational interventions aimed at CGs of \npatients with dementia and to evaluate the usability of iGeriCare learning modules. This \nthesis is comprised of two phases, a systematic literature review and an evaluation of the \niGeriCare lessons. A systematic search was performed on MEDLINE, PubMed, CINAHL, \nand EMBASE. 31 articles and 23 prospective interventions were included in the final \nanalysis. These interventions were generally perceived positively by CGs. Despite CG-perceived \nvalue, there is not enough evidence in the literature to clearly state whether online \ninterventions improve CG stress, self-efficacy, or burden. \nThe Quality in Use Integrated Measurement Framework (QUIM) informs usability. Two \nexperienced CGs agreed to participate. After they viewed the iGeriCare lessons on the \neLearning management system (through the web-based system 360 Articulate), they were \ninterviewed via telephone to gather their opinions of the usability of the iGeriCare modules. \nQualitative interview data were analyzed, resulting in the following themes: relevance of \ncontent and information, slide design, ease of navigation, forward learning, educational \ntools, and accessibility. They perceived iGeriCare as an effective tool with online \nconvenience and relished the thought of an online community whereby CGs can interact in \na spirit of comradery and togetherness.
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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 | 0.020 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".