Longitudinal mixed methods study assessing caregivers of seniors across diverse populations: research protocol
Bibliographic record
Abstract
BACKGROUND: Canada's aging population is increasing, along with the number of caregivers providing support to seniors. Caregiving is a taxing responsibility that often results in loneliness and distress. Creating awareness of available supports for caregivers is essential for their health and to provide the best support to the care recipients. This study aims to better understand and improve the caregiving experience for caregivers from diverse ethnic communities and the LGBTQI2S+ communities. The goal is to improve the well-being and resilience of caregivers and optimize outcomes for care recipients by delivering educational workshops that resemble the design of existing workshops currently offered by the participating social service agency. Content will be adapted based on identified participant learning needs. These workshops will be offered to the English-speaking community, diverse newcomer ethnic groups and the LGBTQI2S+ community. METHODS: This mixed-methods, longitudinal study includes two streams of caregivers; Stream One consists of English-speaking caregivers and care recipients while Stream Two includes individuals from the Afghan, Iranian, Somali-, Tamil- and Spanish-speaking populations and those belonging to LGBTQI2S+ communities. Each stream has two phases; Phase One includes needs assessments using focus groups and semi-structured interviews with caregivers and care recipients while Phase Two includes a pre-test post-test evaluation of educational workshops. The anticipated sample size for Phase One is 30 caregivers from the English-speaking community, 150 from the five linguistic/cultural communities combined and 30 from the LGBTQI2S+ group. For Phase Two, we plan to recruit 250 caregivers from the English-speaking community, 250 from the five linguistic/cultural communities, and 50 from the LGBTQI2S+ group. DISCUSSION: To provide caregivers with optimal support, we must acknowledge the caregivers and care recipients from diverse communities. Currently, at least two focus groups have been conducted with caregivers from each of the seven targeted groups and workshops have begun for all communities. Recruitment has been a challenge for all groups, but our team continues to conduct outreach with caregivers and will use our learning to inform the delivery of educational caregiver workshops.
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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 | 0.049 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".