The case for and Against Doing Virtual Photovoice
Bibliographic record
Abstract
Photovoice offers creative participatory action methods for conveying community strengths and challenges with the goal of addressing health inequities. Accelerated by COVID-19 restrictions, photovoice has increasingly become virtual, and this shift has given rise to new considerations including navigating online recruitment and data collection, e-participatory action trends and working with multi-site large qualitative data sets. Within these contexts, the current article discusses the case for and against virtual photovoice, drawing from a large study comprising 110 men’s experiences of, and perspectives about, equitable and sustainable intimate partner relationships. The findings are shared across three themes. The first theme, e-Efficiencies and concessions contrasts increased recruitment reach and data collection cost-savings with vulnerabilities to phishing and challenges for working with participants’ wide-ranging internet literacies and practices. Theme two, Participatory action changed, chronicles the participants’ varied relationships to photography including sourcing third-party and archived photographs. Revealed also were privacy concerns whereby some participants opted for audio only interviews and/or restricted the use of their photographs. The third theme, Reckoning breadth and depth in a large dataset, discusses emergent study design considerations including analytics for interpreting and contextually representing large multi-site projects that are made possible through virtual photovoice. While technological advances and COVID-19 have forged photovoice virtually, the case for and against this trend reveals complex considerations that will likely manifest a continuum of approaches ranging virtual, hybrid and in-person models. In summary, we suggest that integral to weighing the case for and against virtual photovoice researchers will need to thoughtfully adapt to changing technologies, as well as potential post COVID-19 tilts for returning to in-person.
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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 | 0.104 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.069 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".