Why and how is photovoice used as a decolonising method for health research with Indigenous communities in the United States and Canada? A scoping review
Bibliographic record
Abstract
Globally, including in North America, Indigenous populations have poorer health than non-Indigenous populations. This health disparity results from inequality and marginalisation associated with colonialism. Photovoice is a community-based participatory research method that amplifies the voices of research participants. Why and how photovoice has been used as a decolonising method for addressing Indigenous health inequalities has not been mapped. A scoping review of the literature on photovoice for Indigenous health research in the United States and Canada was carried out. Five electronic databases and the grey literature were searched, with no time limit. A total of 215 titles and abstracts and 97 full texts were screened resulting in 57 included articles. Analysis incorporated Lalita Bharadwaj's Framework For Building Research Partnerships with First Nations Communities. Photovoice was selected to improve knowledge mobilisation and participant empowerment and engagement. Studies incorporated relationship building, meaningful data collection, and public dissemination but had a lesser focus on the inclusion of Indigenous peer researchers or participant involvement in analysis. For photovoice to truly realise its decolonising potential, it must be incorporated into a broader participatory and decolonising research paradigm. In addition, more resources are required to support the involvement of Indigenous people in the research process.
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 | 0.024 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".