Bibliographic record
Abstract
Background: Recent research has found that family (e.g., informal, unpaid) caregivers to those in long-term care can experience significant risk of social isolation, a harmful social outcome linked to poor health and wellbeing. For many, the COVID-19 global pandemic has been a time marked by challenges that have exacerbated existing risk of social isolation and has likely impacted mental health and wellbeing among caregivers. As such, this paper outlines a protocol to investigate the extent to which the COVID-19 pandemic has impacted the psychological health and well-being of family caregivers of people living in residential long-term care. Methods/Design: A descriptive phenomenological design and photovoice methodology will be used alongside focus groups to capture the perspectives and voices of 15-20 family caregivers. Data will be analyzed thematically, and themes will be developed collaboratively alongside participants. A secondary analysis will be guided by a cumulative inequality lens to consider how the COVID-19 pandemic has differentially affected caregivers. Discussion: The results will fill a significant gap in the existing literature on caregiver isolation during this pandemic and inform the development and/or refinement of caregiver supports.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.004 |
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".