Photovoice as a Social Transformative Tool: Unpacking the Experiences of Immigrants and Refugees Living with HIV in Canada
Bibliographic record
Abstract
1026 immigrants and refugees tested positive for HIV (IRLWH) in Canada in 2018 (Haddad, et al, 2019). IRLWH experience discriminatory behaviors because of because of the immigration and HIV status; culturally appropriate supports and resources for IRLWH are lacking. Financial difficulties are experienced by many new immigrants, they may be unable to meet their health care or mental health needs, particularly if they are IRLWH (Chen et al., 2015). Language barriers, loss of social support and a lack of health coverage can impact the ability for IRLWH to access care (Rapid Response Service, 2014). There can be stigma surrounding HIV within the cultural community, impacting the level of support for IRLWH (Rapid Response Service, 2014). IRLWH experience mistreatment by service providers, lack of culturally and linguistically appropriate services, lack of awareness of local programs, unemployment and housing issues in Canada (Chen et al., 2015; Gatteri et al., 2020). To augment the limited extant knowledge on the challenges of IRLWH and based on the implications of a study that claimed the need for a further research exploring the voices of IRLWH using photovoice (Getteri, et.al., 2020), this community based photovoice study was designed with an aim to understand intersectional oppressions experienced by IRLWH across Alberta in general, with a focus on the COVID-19 pandemic in particular from determinants of health perspectives. Keywords: Immigrants and Refugees; HIV, Photovoice, Intersectional Violence, Mental Health
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".