A Comparative Case Study: Exploring Health System Governance in Canada and Saudi Arabia
Bibliographic record
Abstract
Health systems and health system outcomes are incredibly complex. To understand how they function, researchers explore individual components of the system, in the study herein the component is ‘governance’. Research to date has demonstrated a positive relationship between governance and population health outcomes. Governance, therefore, may be a concept that assists in understanding differential health outcomes of seemingly comparable countries. This study aims to explore macro-level governance, particularly the two sub-concepts of ‘government effectiveness’ and ‘perceived corruption’, in two countries: Saudi Arabia and Canada. Government effectiveness reflects the quality of public health policy development and implementation, and how much the government adheres to these policies. These comparator countries are selected as they share similarities on three levels, economy, population size, and free basic healthcare; yet differ significantly in governance models. A case study methodology as described by Stake (1995), guided this study. This study is particularly a comparative case study design with a focus on qualitative data. The data will be used to understand in-depth nuances of governance in health systems. Two overarching questions guided this study, one for each of the sub-concepts: 1) How the government effectiveness process, in terms of health policy development and implementation, unfolds within the health system in Saudi Arabia and Canada. 2) How corruption, as an aspect of governance, is present within health systems. This work is framed within a critical theoretical perspective. Concerns about good governance and corruption that guide this work is to the purpose of seeking the best health outcomes for all people. Governance as a whole, and sub-concepts of government effectiveness and corruption, are all amenable to change and improvement. To engage with system complexity, multiple data sources were utilized within this case study. Primary data consisted of interviewing 32 participants (15 in Canada and 17 in Saudi Arabia) who work in the health system in service provision, research, policy, management, or education. Secondary data included government documents about health system structure and strategies at the macro level. Data collection was conducted through two phases. Phase one of data collection involved in-depth interviews with experts across the health systems. The interviews were conducted in both English and Arabic. Documents for analysis were collected and accessed through official websites of governments or Ministries of health, and healthcare organizations, and scientific databases. These documents were analyzed via Critical Discourse Analysis (CDA) as outlined by Van Dijk (1993) and Mullet (2018). The findings are divided into three foci as three chapters: 1) a methodological piece on conducting bilingual research; 2) the nature of government effectiveness; and 3) the nature of corruption in health systems. Conducting research in a language not spoken by all the research team members is relatively common, yet addressing the nuanced details of implementing bilingual work has limited guidance within extant literature. This includes consideration of promising practices for concept development, translation, data analysis, and presenting the findings. This chapter is an exploration of the strengths and limitations of doing bilingual research, and recommendations regarding these aforementioned issues from our own experiences. Ultimately, it is proposed that via bilingual research, the accumulation of knowledge pertaining to qualitative research concepts, translation, analysis, and dissemination of comprehensive frameworks can be enacted, ultimately enhancing the rigour of qualitative research and increasing confidence in applying knowledge created in the chosen language of participants. Findings on government effectiveness in health systems in both Canada and Saudi Arabia are presented in four themes. These four themes are: 1) Health is Political, 2) Health System Privatization, 3) An Outdated System vs. A System that is Catching Up, and 4) Social Determinants of Health (SDoH) and Cross-Sectoral Collaboration. Recommendations are provided on how to better identify elements of government effectiveness and integrate them with the SDoH in order to enhance system effectiveness and improve the health of populations. For the chapter on corruption, it is noted that Governance is a complex theoretical concept that includes the sub-concept of ‘corruption’. A very ‘loaded’ term, this study sought to understand how corruption is present in health systems, often in very subtle ways. Findings illustrate how corruption is still a relevant concept in advanced health systems and can include both subtle and even overt forms within Canadian and Saudi health systems. This is explained in three themes: 1) Corruption in Wealthy Nations: Subtle Opportunism; 2) Nepotism and Professional Courtesy; and 3) A Strict System vs A Relaxed System. This analysis uncovers nuanced forms of potential personal gain within Canadian and Saudi health systems that make the concept of corruption still a timely concern. Addressing these risks must be seen as a collective obligation, where healthcare providers identify and report cases of potential corruption, managers prevent and address opportunities for personal gain, and researchers study how to develop policies and processes that are most immune to corruption. Ultimately, this study continues to unpack the complex ways that health systems are actualized, looking particularly at the concept of governance, and selected sub-concepts of government effectiveness and corruption.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".