Using photovoice methods to explore older people's perceptions of respect and social inclusion in cities: Opportunities, challenges and solutions
Bibliographic record
Abstract
Urbanisation and population ageing have contributed to recognise cities as important settings for healthy ageing. This paper considers opportunities, challenges and solutions of using photovoice methods for exploring how individuals perceive their cities and the contribution this makes to their health. It focuses on one aspect of older people's experiences - respect and social inclusion, in the context of a community-based participatory research. Drawing on selected findings (participants' photographs, associated quotes and researchers' field notes), we provide an assessment of the suitability of photovoice methodology for the intended purpose. Four groups of older people (n=26; aged 60 years or more) from four contrasting geographical areas in Liverpool, UK, were recruited purposively. Participants photographed perceived positive and negative aspects of respect and social inclusion in the city, reflecting on the meanings of the photographs in individual (n=23) and group interviews (n=9). Thematic and content analysis was conducted using NVivo 10 software. The work reported here provides insights into how participants engage with the photovoice process; factors preventing taking photos of interest; and how photographs complement interviews and focus groups. The findings demonstrate that photovoice both facilitated the dissemination of personalised relevant knowledge, and encouraged critical dialogue between participants, and city stakeholders. Reported difficulties included photography of negative and social concepts, and anxiety when taking photographs due to (i) expectations of what is a 'proper' photograph, and (ii) the need to obtain consent from subjects. With preparation, training, and discussion of participants' ideas not expressed through photographs, photovoice was well-suited to this topic, providing insights complementing other research methods. Through analysing the application of photovoice for exploring perceptions of respect and social inclusion in cities, our paper has identified potential issues and provides important recommendations for researchers on how photovoice methodology can be strengthened in exploring conditions for better health in the urban environment.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".