Using Photovoice as a Method for Capturing the Lived Experiences of Caregivers During COVID-19: A Methodological Insight
Bibliographic record
Abstract
Although the extant literature identifies photovoice as one of the most innovative and creative research methods that encourage reflection and introspection, few studies have described the use of photovoice with family/informal caregivers. This paper discusses the implementation of photovoice as a novel approach in exploring the experiences of informal caregivers ( n = 10) of older adults in long-term care homes during the COVID-19 pandemic. The article describes the four stages of the photovoice process undertaken: (1) preparation; (2) pre-focus group meeting; (3) taking photographs; and (4) reflection and implementation insights, to researchers. The different stages in the research process inspired several key learnings, including the use of co-learning tools, the valuable combination of photographic images and words to provide rich description of participants’ perspectives, and creative ways to engage and support caregivers in sharing their stories. This paper also addresses some practical challenges of using this methodology with informal caregivers and explore issues surrounding research ethics and photographs. Knowledge gained from this case example provides strong support for the use of photovoice as a creative approach to better illuminate and understand the experiences of caregivers and can inform the design of future virtual studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".