Intergenerational Healing Through Connection: The Need for More Indigenous Patient Services in Ontario Hospitals
Bibliographic record
Abstract
INTRODUCTION: In Ontario, there is a serious lack of tangible services for Indigenous patients in hospitals. The resources that are available for Indigenous people are difficult to find and not as accessible as they should be. Despite the clear need for accessible and plentiful Indigenous health resources, our hospitals are filled with the empty promises from the Truth and Reconciliation Commission: Calls to Action.\nPURPOSE: I created this report to fulfil three tasks: 1 - to increase accessibility of Indigenous health services currently offered in Ontario, 2 - to provide a list of Ontario hospitals with Indigenous resources and to identify the percentage of hospitals without them, and 3 - to create a resource that hospitals can have on their website, which will direct Indigenous patients towards care that is safe, inclusive, and appropriate.\nMETHODS: Using the Ontario Government website with a list of all the hospitals in Ontario, I went to each hospital's websites and typed in the word “Indigenous” into the search engine. On the Ontario Government Health Website, 138 hospitals were listed of which 121 were included in the study. Then, I recorded the hospitals with resources and services for their Indigenous patients.\nRESULTS: Only 23.3% of Hospitals in Ontario have tangible Indigenous services for their patients. The highest number of Hospitals with Indigenous services were found in Western Ontario with 42.8% of hospitals having services. This is contrasted with the lowest number of Hospitals with Indigenous services in Toronto, Ontario at 21.4% and Eastern Ontario at 27.5%.\nCONCLUSION: One goal of the TRC’s: Calls to Action was to have all hospitals in Canada with Indigenous healing accessible to their patients. As we can see, 7 years after the publication of the Calls to Action, only 23.3% of hospitals in Ontario alone have accomplished this goal. Moreover, our lowest Indigenous services are within our Provincial and Federal capital cities, which demonstrates the lack of commitment towards funding these programs. I hope that my guides can be used as a resource for Indigenous patients and can also be placed on hospital websites, as a way to increase access to Indigenous Health Programs in Ontario. I also hope that this study acts as inspiration to increase Indigenous health services in Ontario Hospitals.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".