Performance management in healthcare: a critical analysis
Bibliographic record
Abstract
Purpose - The purpose of this paper is to explore the underlying theoretical assumptions and implications of current micro-level performance management and evaluation (PME) practices, specifically within health-care organizations. PME encompasses all activities that are designed and conducted to align employee outputs with organizational goals. Design/methodology/approach - PME, in the context of healthcare, is analyzed through the lens of critical theory. Specifically, Habermas' theory of communicative action is used to highlight some of the questions that arise in looking critically at PME. To provide a richer definition of key theoretical concepts, the authors conducted a preliminary, exploratory hermeneutic semantic analysis of the key words "performance" and "management" and of the term "performance management". Findings - Analysis reveals that existing micro-level PME systems in health-care organizations have the potential to create a workforce that is compliant, dependent, technically oriented and passive, and to support health-care systems in which inequalities and power imbalances are perpetually reinforced. Practical implications - At a time when the health-care system is under increasing pressure to provide high-quality, affordable services with fewer resources, it may be wise to investigate new sector-specific ways of evaluating and managing performance. Originality/value - In this paper, written for health-care leaders and health human resource specialists, the theoretical assumptions and implications of current PME practices within health-care organizations are explored. It is hoped that readers will be inspired to support innovative PME practices within their organizations that encourage peak performance among health-care professionals.
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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 | 0.061 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".