Systems Thinking and Human Resource Management in Healthcare: A Scoping Review of Core Applications Across Health System Levels
Bibliographic record
Abstract
Background: Systems thinking (ST) is an approach to problem-solving that views systems through a holistic perspective, focusing on the interconnections and relationships between various elements. In healthcare, the World Health Organization’s 2009 report marked a paradigm shift toward ST, prompting the development and use of ST tools to address complex challenges. Despite this, limited attention has been given to ST’s application in healthcare human resource management (HRM). This paper aims to provide a scoping review of ST application in healthcare HRM to explore its value in workforce management. Methods: Following Arksey and O’Malley’s framework, a scoping review was conducted to map how ST has been applied in healthcare HRM. Peer-reviewed articles published between 1999 and December 2024 were identified through Scopus and PubMed, using search terms such as systems thinking, human resources, and workforce. Data were extracted using a structured tool, and findings were analyzed through the lens of the system level of application. Results: The review identified 19 studies from 15 countries, with the majority using qualitative or mixed methods approaches across diverse settings. Core applications were applied at the macro, meso, and micro system levels to address workforce challenges, map feedback loops, identify leverage points, and strengthen stakeholder collaboration. ST was commonly applied at regional and national levels and supported improved workforce planning, policy development, and service coordination. Most studies employed soft systems modeling. Conclusions: This review highlights ST’s potential to enhance HRM by recognizing interdependencies across workforce functions. Findings suggest that ST enables more integrated strategies, promotes collaboration, and supports systemic decision-making. The adoption of ST in healthcare HRM may address persistent workforce challenges, though implementation remains limited by reductionist perspectives and unfamiliarity with ST tools.
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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 | 0.019 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".