Reviewing Inequities in Primary Care Received by Indigenous Peoples in Ontario
Bibliographic record
Abstract
Research has shown that Indigenous peoples in Canada experience health inequities when compared to the non-Indigenous population. High quality primary care has been described in literature; however, this has not been explored through the lens of Indigenous health. A scoping review was performed in order to investigate the quality of primary care received by indigenous peoples in Ontario. To conduct this review, a search of current literature on primary care in Indigenous communities in Ontario was performed. The studies examined in this review were derived from four different databases and many evaluated specific communities using a qualitative and quantitative approach. Several themes were identified including inadequate preparation and training of health care providers, physician and nursing shortages, strategies associated with improved quality of care, management of mental health, disparities in health service delivery station types and ineffective primary care impacts on hospitalizations. This literature search demonstrated a clear gap in the literature on the quality of primary care received by the Indigenous population in Ontario. Thus, further research is necessary in order to outline the current state of primary care being delivered to Indigenous populations in Ontario, and develop strategies to enhance the quality of care for this population.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.024 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".