First Nations health policy and funding: consequences for those living With HIV/AIDS
Bibliographic record
Abstract
In 2014-2015 Indigenous Peoples represented 17.5% of all HIV infections in Canada, yet accounted for only 4.3% of the population. In 2008, Indigenous Peoples accounted for an estimated 3.2% of people living with HIV in Ontario, while comprising 2.4% of the population. From 2009 to 2011, 2.7% of new HIV diagnoses in Ontario were Indigenous Peoples, of whom 7.2% were women. This research study sought to assess the efficacy of funding for HIV/AIDS treatment, services, programming, and care within Ontario First Nations communities. This research will improve understanding of services available to people and communities affected by the HIV/AIDS epidemic. The Indigenous based method of storytelling and freedom of information requests were used to capture data. Ontario First Nations people who were at least 16 years of age and living with HIV/AIDS (n=29) participated. Participants were asked five open-ended questions related to their use of and access to healthcare services. Stories were transcribed and analysed using NVivo. Transcriptions also form the bases of re-written first-person stories, detailing the life and experiences of the participants and their experiences of living with HIV/AIDS and accessing treatment, services, programming, and care. It was found that the federal government drastically underfunds HIV/AIDS treatment and services. This is given context by powerful stories of the impact limited funding has on Indigenous people living with HIV/AIDS. Participants experienced issues with access to care and supports with many forced to leave their northern communities, either permanently or temporarily, due to limited access to care. HIV-related stigma played a role in access to prevention, testing, and care. Participants indicated difficulties with HIV education either in understanding their own HIV status or in the lack of education within the broader community. Historical traumas (residential schooling and the 60s scoop) and discrimination were central themes to many stories, seriously affecting the lives of participants and their overall health outcomes. The dissertation/project culminates in a list of recommendations aimed at informing a process to improve access and quality of health care for Indigenous Peoples living with HIV/AIDS. Greater access to community-based, holistic care in northern First Nations communities is urgently required.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".