Photovoice in mental illness research: A review and recommendations
Bibliographic record
Abstract
In the past few decades, photovoice research has gained prominence, providing context rich insights through participants' photographs and narratives. Emergent within the field of photovoice research have been health studies embracing diverse illness issues. The goal of this scoping review article was to describe the use of photovoice in mental illness, paying particular attention to the following: (1) the study design and methods, (2) empirical findings, and (3) dissemination strategies. Nine qualitative studies (seven drawing from primary and two secondary analyses) featuring diverse approaches to analysis of data comprising individual and/or focus group interviews using participant-produced photographs were included in the review. Described were participant's experiences of living with mental illness and/or substance overuse, including feelings of loneliness and being marginalized, along with their support care needs (e.g. physical, emotional, and spiritual) to garner self-confidence, respite, and/or recovery. Empirically, the reviewed articles confirmed the value of participant-produced photographs for obtaining in-depth understandings about individual's mental illness experiences while a focus on stigma and recovery was prominent. In terms of dissemination, while most of the published articles shared some participants' photographs and narratives, less evident were strategies to actively engage the public or policymakers with the images. Recommendations for future photovoice research include conducting formal analyses of participant photographs and strategically lobbying policymakers and raising public awareness through virtual and "in person" photo exhibitions while de-stigmatizing and affirming the experiences of those who are challenged by mental illness.
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".