Strengthening Strategic Reward Framework in Health Systems: A Survey of Narok County, Kenya
Bibliographic record
Abstract
BACKGROUND: Rewards are used to strengthen good behavior among employees based on the general assumption that rewards motivate staff to improve organizational productivity. However, the extent to which rewards influence motivation among health workers (HWs) has limited information that is useful to human resources (HRs) instruments. This study assessed the influence of rewards on motivation among HWs in Narok County, Kenya. METHODS: This was a cross-sectional study done in two sub-counties of Narok County. Data on the rewards availability, rewards perceptions and influence of rewards on performance, as well as motivation level of the HWs, was collected using a self-administered questionnaire with HWs. SPSS version 21 was used to analyze descriptive statistics, and factor analysis and multivariate regression using Eigen vectors was used to assess the relationship between the reward intervention and HWs’ motivation.RESULTS: A majority of HWs 175 (73.8%) had not received a reward for good performance. Only 3 (4.8%) of the respondents who received rewards were not motivated by the reward they received. Overall, reward significantly predicted general motivation (p-value = 0.009).CONCLUSION: In Narok County, the HR’s instruments have not utilized the reward system known to motivate employees. In the study area, hard work was not acknowledged and rewarded accordingly. In addition, there were not sufficient opportunities for promotion in the county. An increased level of reward has the potential to motivate HWs to perform better. Therefore, providing rewards to employees to increase motivation is a strategy that the Narok County health system and its HR management should utilize.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".