Developing a Tool to Measure Health Worker Motivation in District 开发一个工具来测量区卫生工作者积极性.pptVIP

Developing a Tool to Measure Health Worker Motivation in District 开发一个工具来测量区卫生工作者积极性.ppt

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Developing a Tool to Measure Health Worker Motivation in District 开发一个工具来测量区卫生工作者积极性

Developing a Tool to Measure Health Worker Motivation in District Hospitals in Kenya Patrick Mbindyo, Duane Blaauw, Lucy Gilson, Mike English Background Interventions to improve worker practice may fail if HWs are poorly motivated Worker motivation strongly linked to worker performance Tool development (SAQ) – to characterize average motivation in hospitals Simple to apply can quantitatively describe hospitals Nested in larger intervention study seeking to improve worker practices in DH Methods To measure motivation, use subjective or objective methods? Literature review done to source possible constructs for inclusion 17 constructs used- 10 determinants; 7 outcomes Result in SAQ with 71 questions: 5 point Likert scale – strongly agree to strongly disagree Response bias - randomly assorted 40% negatively worded Piloted to explore its performance – reduced questions to 66 Outcomes – 23 questions Sampling Data Collection Convenience sample of 90 staff from each hospital (30% of hosp staff) Random sample constraints -staff leave, breaks, shift Total sample size estimate of 720 Data collected by identically trained survey teams on-site (2 weeks per site) Preferentially targeted staff in paediatric areas other areas with regular contact with sick children Intervention was aimed at improving paediatric care. Data Analysis Data analysed using STATA 9.2. Scoring of Likert scale responses: 1-5 (where strongly agree is 5 for positively worded qns). 1-5 (reverse coding for negatively worded qns- strongly disagree is 5) I am confident about my ability to do my job (+ve) I cannot be relied upon by my colleagues at work (-ve) Simple stats used to explore performance of SAQ - frequency distributions, mean and median scores Further analysis done – Correlation (0.5 and above good); Cronbach’s alpha; Factor analysis – data reduction + simplified index Results Respondent Characteristics Hospitals Total (%) X2 P value H1 H2 H3 H4 H5 H6 H7 H8 Gender (female

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