Representing informal caregivers of older adults in occupation-focused research: A critical interpretive synthesis
Bibliographic record
Abstract
Within the current demographic and political context, it is likely there will be increasing reliance on informal caregivers in the provision of care to older adults in Western nations. This critical interpretive synthesis explored how informal caregiving has been conceptualized and researched in the occupation-based literature. Five occupation-focused journals were searched for articles on informal caregiving for older adults, resulting in 17 primary research articles. Analysis of these articles revealed that there has been an increasing focus on the experiences of caregivers (as opposed to divided focus between caregivers and care recipients) and that transactional perspectives and exploration of co-occupations have expanded the scope of the literature in this field. This synthesis points to a need to turn greater attention to diversity among informal caregivers, particularly in relation to gender and gender identity and raises concerns regarding lack of critical attention to informal caregiving as embedded in social relations of power. An occupational perspective can contribute to research on informal caregiving by generating knowledge regarding how this occupation is shaped in relation to contextual and political elements and has the potential to illuminate implications for the occupations of caregivers and care recipients.
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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 | 0.109 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.029 | 0.018 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".