Holistic experiences of Indigenous carer-employees in Southern Ontario
Bibliographic record
Abstract
This qualitative study uses Indigenous ethical protocols and the Medicine Wheel as a guiding framework for deductive coding to examine the mental, physical, emotional, and spiritual care provided by ten Indigenous Carer-Employees (CEs) living and working in Ontario. CEs are individuals who work in paid employment while providing unpaid care to family, friends, and/or relatives with physical, mental and/or cognitive disability or challenges. The paper adopts a holistic perspective by acknowledging the Medicine Wheel's four aspects—spiritual, emotional, mental, and physical well-being—to understand the nature of care provided by these CEs to their loved ones from an Indigenous perspective. This care manifests in a plethora of ways, including the importance of aiding loved ones with intellectual tasks, the need to provide physical labour, the importance of providing a safe emotional space for loved ones, and the role of helping with spirituality in the caregiving practice. The role of the workplace, including policies, experiences, and the differences between Indigenous and non-Indigenous employers, is also explored through inductive thematic analysis. This paper contributes to the growing literature on CEs and contemporary Indigenous caregiving. It provides suggestions for future scholarship to elevate the voices of Indigenous CEs, the importance of Carer-Friendly Workplace Policies, and the necessity to consider participant positionality in all research, particularly in research involving Indigenous participants, where the impacts of colonialism continue to pervade into many aspects of daily life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".