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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".