Chronic pain experiences of immigrant Indian women in Canada: A photovoice exploration
Bibliographic record
Abstract
Background: Over the past two decades, the prevalence of chronic pain has significantly increased globally, with approximately 20% of the world's population living with pain. Although quantitative measures are useful in identifying pain prevalence and severity, qualitative methods, and especially arts-based ones, are now receiving attention as a valuable means to understand lived experiences of pain. Photovoice is one such method that utilizes individuals' own photography to document their lived experiences. Aims: The current study utilized an arts-based method to explore immigrant Indian women's chronic pain experiences in Canada and aimed to enhance the understanding of those experiences by creating a visual opportunity for them to share their stories. Methods: Twelve immigrant Indian women captured photographs and participated in one-on-one interviews exploring daily experiences of chronic pain. Results: Women's photographs, and description of these photographs, provided a visual entry into their lives and pain experiences. Three themes emerged from our analysis: (1) bodies in pain, (2) traversing spaces including immigration, and (3) pain management methods. Findings revealed that women's representations of pain were shaped by a clash between culturally shaped gender role expectations and changing gender norms due to immigration processes. The use of photovoice visually contextualized and represented pain experiences, proving to be a valuable tool for self-reflection. Conclusions: This research uncovers the multifaceted nature of chronic pain and identifies the influence of immigration, gender, and social relations on the exacerbation of pain in immigrant Indian women.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".