EXPLORING ARTS-BASED METHODS IN UNPAID CAREGIVING CONTEXTS: A SCOPING REVIEW
Bibliographic record
Abstract
Introduction:Unpaid caregiving plays a crucial role in supporting older adults, requiring innovative approaches to understand and enhance caregivers' experiences. Research Objectives: As a response, this scoping review investigates the application of arts-based methods in understanding the caregiving experience, as well as the advantages and limitations of these methods in providing care to diverse populations. Methodology: Building on Arksey and O’Malley's framework, our methodology draws from the Joanna Briggs Institute approach. Eligibility criteria encompassed peer-reviewed publications in English from 2007 to 2022, focusing on unpaid caregivers, arts-based methods, and caregiving experiences. Six comprehensive databases were queried, with the initial searches yielding 761 articles. Full text screening of 66 papers resulted in 19 papers for inclusion in the scoping review. Results: Analysis of the literature revealed the multifaceted applications of arts-based methods in understanding and supporting unpaid caregivers. These encompassed a wide range of creative techniques, such as visual arts, photo elicitation, storytelling, and performance – which provided insights into the emotional, psychological, and social dimensions of caregiving. Findings highlighted the potential for arts-based methods to enhance caregiver well-being, foster self-reflection and self-care, and promote dialogue. Nonetheless, there were notable gaps. Racial and ethnic diversity was often neglected in the studies (addressed in 3 of 19), underscoring the necessity for tailored interventions and methodological considerations for diverse populations. Importantly, dementia caregiving received substantial attention (12 of 19). Final Considerations: This scoping review highlights the growing interest in integrating arts-based methods into caregiving research and interventions amongst qualitative and mixed methods approaches. By mapping out the landscape of existing literature, this review underscores the need for further exploration and methodological refinement to harness the full potential of creative techniques in advancing our understanding of unpaid caregivers' lived experiences. Arts-based approaches hold promise in providing a nuanced and holistic perspective on unpaid caregiving experiences.
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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 | 0.024 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".