Photovoice as a Method for Revealing Community Perceptions of the Built and Social Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the last number of years there has been growing interest in the use of community-based participatory research (CBPR) for preventing and controlling complex public health problems. Photovoice is one of several qualitative methods utilized in CBPR, as it is a participatory method that has community participants use photography, and stories about their photographs, to identify and represent issues of importance to them. Over the past several years photovoice methodology has been frequently used to explore community health and social issues. One emerging opportunity for the utilization of photovoice methodology is research on community built and social environments, particularly when looking at the context of the neighbourhood. What is missing from the current body of photovoice literature is a critique of the strengths and weaknesses of photovoice as a method for health promotion research (which traditionally emphasizes capacity-building, community-based approaches) and as a method for revealing residents' perceptions of community as a source of health opportunities or barriers. This paper will begin to address this gap by discussing the successes and challenges of using the photovoice methodology in a recent CBPR project to explore community perceptions of the built and social environment (with the ultimate goal of informing community-based chronic disease prevention initiatives). The paper concludes with methodological recommendations and directions for future research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it