“We’re sinking”: a qualitative interview-based study on stakeholder perceptions of structural and process limitations to the Canadian healthcare system
Bibliographic record
Abstract
Abstract Background Despite longstanding efforts and calls for reform, Canada’s incremental approach to healthcare changes has left the country lagging behind other OECD nations. Reform to the Canadian healthcare system is essential to develop a higher performing system. This study sought to gain a deeper understanding of the views of Canadian stakeholders on structural and process deficiencies and strategies to improve the Canadian healthcare system substantially and meaningfully. Methods We conducted individual, ~ 45-minute, semi-structured virtual interviews from May 2022 to August 2022. Using existing contacts and snowball sampling, we targeted one man and one woman from five regions in Canada across four stakeholder groups: (1) public citizens; (2) healthcare leaders; (3) academics; and (4) political decision makers. Interviews centered on participants’ perceptions of the state of the current healthcare system, including areas where major improvements are required, and strategies to achieve suggested enhancements; Donabedian’s Model (i.e., structure, process, outcomes) was the guiding conceptual framework. Interviews were audio-recorded, transcribed verbatim, and de-identified, and inductive thematic analysis was performed independently and in duplicate according to published methods. Results The data from 31 interviews with 13 (41.9%) public citizens, 10 (32.3%) healthcare leaders, 4 (12.9%) academics, and 4 (12.9%) political decision makers resulted in three themes related to the structure of the healthcare system (1. system reactivity; 2. linkage with the Canadian identity; and 3. political and funding structures), three themes related to healthcare processes (1. staffing shortages; 2. inefficient care; and 3. inconsistent care), and three strategies to improve short- and long-term population health outcomes (1. delineating roles and revising incentives; 2. enhanced health literacy; 3. interdisciplinary and patient-centred care). Conclusion Canadians in our sample identified important structural and process limitations to the Canadian healthcare system. Meaningful reforms are needed and will require addressing the link between the Canadian identity and our healthcare system to facilitate effective development and implementation of strategies to improve population health outcomes.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".