Motivating health professionals through control mechanisms: A review of empirical evidence
Bibliographic record
Abstract
This paper summarizes the findings of the literature on the levers used in the health care sector to motivate workers, with a particular focus on the impact of management control tools (such as Performance Measurement Systems (PMS) and Pay for Performance) on motivation. A review of the literature was carried out using the ISI Web of Knowledge, Pubmed and JSTOR search engines on the topic of motivation of health care workers, including, if possible, all the involved categories of employees. The research focused on empirical studies published in Europe, North America and Oceania from 1990 to 2015. Developing countries were intentionally excluded because of their specific needs and motivation perspectives that mainly focus on recruitment or retention strategies to ensure services provision. Studies on motivation generally focus on three main perspectives: (1) Employees’ satisfaction and emotions; (2) Retention; (3) Motivation or attitudes to carry out specific tasks or to behave appropriately. A few studies considered compensation strategies and monetary rewards as a driver of health care workers’ motivation. These studies did not report the crowding out effect of external locus of causality on motivation. On the contrary, most of the studies highlighted the importance of the relationship with patients and colleagues as a crucial factor affecting workers’ motivation, in particular referring to job satisfaction. Despite the large number of articles on the topic of employee motivation, there have been very few studies on the impact of the most popular managerial mechanisms introduced since the mid 1990s in health care systems.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".