A Canadian qualitative study exploring the diversity of the experience of family caregivers of older adults with multiple chronic conditions using a social location perspective
Bibliographic record
Abstract
BACKGROUND: A little-studied issue in the provision of care at home by informal caregivers is the increase in older adult patients with chronic illness, and more specifically, multiple chronic conditions (MCC). We know little about the caregiving experience for this population, particularly as it is affected by social location, which refers to either a group's or individual's place/location in society at a given time, based on their intersecting demographics (age, gender, education, race, immigration status, geography, etc.). We have yet to fully comprehend the combined influence of these intersecting axes on caregivers' health and wellbeing, and attempt to do this by using an intersectionality approach in answering the following research question: How does social location influence the experience of family caregivers of older adults with MCC? METHODS: The data presented herein is a thematic analysis of a qualitative sub-set of a large two-province study conducted using a repeated-measures embedded mixed method design. A survey sub-set of 20 survey participants per province (n = 40 total) were invited to participate in a semi-structured interview. In the first stage of data analysis, Charmaz's (2006) Constructivist Grounded Theory Method (CGTM) was used to develop initial codes, focused codes, categories and descriptive themes. In the second and the third stages of analysis, intersectionality was used to develop final analytical themes. RESULTS: The following four themes describe the overall study findings: (1) Caregiving Trajectory, where three caregiving phases were identified; (2) Work, Family, and Caregiving, where the impact of caregiving was discussed on other areas of caregivers' lives; (3) Personal and Structural Determinants of Caregiving, where caregiving sustainability and coping were deliberated, and; (4) Finding Meaning/Self in Caregiving, where meaning-making was highlighted. CONCLUSIONS: The intersectionality approach presented a number of axes of diversity as comparatively more important than others; these included gender, age, education, employment status, ethnicity, and degree of social connectedness. This can inform caregiver policy and programs to sustain health and well-being.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.028 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".