Assessing caregiver stress outcomes among First Nations people: A two-state comparison of caregiver stress outcomes between First Nations people and Whites
Bibliographic record
Abstract
Pearlin's (1989) theory of caregiver stress outcomes model, depicts the effects of ascribed statuses such as race and ethnicity and their influence upon the intensity of stress outcomes. This project investigated Pearlin's (1989) concept of embedded social environments acting as independent variables upon stress outcomes. This study investigated the levels of stress outcomes of caregiving stress between First Nations People caregivers of relatives with dementia or Alzheimer's disease (AD) and White caregivers of relatives with dementia or AD in two states. Stress outcomes, the dependent variable was operationalized from a depression scale in self-appraisals of caregivers who completed a client intake survey as they enrolled in respite care programs in New Mexico and Alaska established through the Alzheimer's Disease Demonstration Grant projects of the federal Agency on Aging. The primary stressors or independent variables from the client intake survey were: functional abilities (Activities of Daily Living and Instrumental Activities of Daily Living), problem behaviors, caregiver tasks, objective burden, subjective stress burden and subjective demand burden scales. Data was collected from N = 84 caregivers within the two states, which included a total of three reservation sites. A primary research question was: what are the levels of stress outcomes among First Nations People compared with stress outcomes among Whites? Considering the social structural strain of being a marginal group, and the effects of generational oppression experienced by First Nations People from the effects of ongoing European colonialism, the main hypothesis states that there would a higher level in stress outcomes for First Nations People caregivers than for White caregivers. Data were analyzed using univariate, and bivariate descriptives, and multiple linear regression analysis. Qualitative historical research provided the contextual background for the structural strains of generational oppression on First Nations People. Regression analyses did not support the hypothesis; there was no significant difference in stress outcomes for First Nations People caregivers than for Whites. This indicates need for future research to explain this phenomenon in light of ongoing disenfranchisement of First Nations People.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".