How experiences affect decision-making: Exploring the phenomenon of access to healthcare through the stories of Indigenous women in British Columbia.
Bibliographic record
Abstract
This thesis explores how Indigenous women experience barriers to accessing healthcare in British Columbia (B.C.) and how their experiences influence their health-related decisions. The intention was to explore how lived experiences with the provincially funded healthcare system affect if at all, Indigenous women’s decision to access healthcare and consider the potential future implications. The phenomenon of access to healthcare for Indigenous women was explored by analyzing pre-existing literature and conducting in-depth qualitative interviews with Indigenous women. The data collected from the interviews were analyzed through Interpretative Phenomenological Analysis. By listening to and amplifying the realities of Indigenous women’s experiences, this research is contributing toward reconciliation. As a non-Indigenous researcher conducting research with Indigenous Peoples, applying and honouring Indigenous research methods and principles of data governance was equally important. Indigenous Researchers at the University of Victoria guided this work along with the CARE principles for Indigenous Data Governance and the Four R’s of Indigenous Research. The analysis indicated the significance of relationality and connection with providers and the system through which Indigenous women access care. It established that these were critical factors affecting their decision-making. Further, this study demonstrates the need for increased understanding and appreciation of Indigeneity within the healthcare systems and the unwavering perseverance that Indigenous women embody to advocate for their and others’ equitable care. This thesis could enrich the development and application of services supporting Indigenous communities and strengthen current healthcare practices and policies by accepting alternative forms of care outside Western healthcare.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".