Effective healthcare coverage in Canada: The caring and responding in Edmonton project
Bibliographic record
Abstract
Despite the progress towards Universal Health Coverage (UHC) in Canada, individuals experiencing socioeconomic disadvantages continue to face barriers to accessing necessary health services. This study explored the observational insights of social care providers (SCPs), who regularly engage with vulnerable populations and healthcare systems, to better understand these barriers in Edmonton, Canada. A qualitative descriptive design was employed, guided by COREQ criteria. Purposive sampling recruited SCPs from 22 community organizations serving Indigenous community members and people experiencing homelessness, substance use, and immigration-related challenges. Deductive thematic analysis, structured around the Effective Coverage (EC) framework, was used to analyze interview data. A coding framework was developed a priori, and inductive coding identified interpretive themes within each EC category. Sixty-five SCPs participated. Findings were organized into four EC categories: service availability, accessibility, acceptability, and utilization. Within these, ten themes were identified: insufficient service capacity, restrictive access policies, service denial, unaffordable care, transportation barriers, racism, stigma, patient-related deterrents to seeking care, language barriers, and barriers to navigating the healthcare system. These themes illustrate how institutional policies, resource limitations, and communication challenges intersect with socioeconomic disadvantages to hinder access to and quality of care. SCPs also described how their organizations often intervene to help clients overcome these barriers. SCPs can provide valuable observational insights into healthcare access barriers. While these perspectives do not represent direct patient accounts, they offer critical input for designing policy interventions to improve UHC. Applying the EC framework may enhance equity and support continuous quality improvement. • Despite ideal universal health coverage in Canada, accessing health services can be challenging. • People experiencing socioeconomic disadvantages are particularly affected. • Many barriers can impact service availability, accessibility, acceptability, and utilization. • Utilizing the effective coverage model to assess these barriers is beneficial. • Health policy interventions might be needed to ensure universal health coverage for all.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".