Table 4.
Province fixed-effects linear regression analysis of associations of generative AI (GenAI) usage, perceived usefulness (PU), and perceived risk (PR) with subscales of burnout among Chinese physicians.
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Work exhaustion | Interpersonal disengagement | ||||||||||||
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β (95% CI) | P value | β (95% CI) | P value | ||||||||||
| Modela | ||||||||||||||
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The frequencies of using GenAI | |||||||||||||
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Mostly | Reference | Reference | Reference | Reference | ||||||||
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Occasionally | 0.12 (–0.08 to 0.32) | .24 | 0.00 (–0.18 to 0.19) | .98 | ||||||||
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Rarely | 0.13 (–0.09 to 0.35) | .25 | –0.06 (–0.26 to 0.14) | .56 | ||||||||
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Never | 0.04 (–0.20 to 0.27) | .75 | –0.12 (–0.34 to 0.09) | .25 | ||||||||
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PU | –0.00 (–0.09 to 0.09) | >.99 | –0.03 (–0.11 to 0.05) | .50 | |||||||||
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PR | 0.37 (0.30 to 0.44) | <.001 | 0.34 (0.28 to 0.41) | <.001 | |||||||||
| Modelb | ||||||||||||||
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The frequencies of using GenAI | |||||||||||||
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Mostly | Reference | Reference | Reference | Reference | ||||||||
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Occasionally | 0.05 (–0.14 to 0.24) | .59 | –0.09 (–0.27 to 0.10) | .35 | ||||||||
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Rarely | 0.08 (–0.13 to 0.29) | .46 | –0.16 (–0.36 to 0.04) | .11 | ||||||||
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Never | 0.05 (–0.18 to 0.28) | .66 | –0.18 (–0.40 to 0.03) | .09 | ||||||||
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PU | 0.06 (–0.03 to 0.14) | .21 | –0.00 (–0.09 to 0.09) | .97 | |||||||||
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PR | 0.27 (0.20 to 0.34) | <.001 | 0.27 (0.19 to 0.34) | <.001 | |||||||||
aCrude models.
bModels adjust for age, gender, marital status, ethnicity, salary satisfaction, professional title, years in practice, night shift, weekly patient volume, specialty, teaching assessment pressure, research assessment pressure, and province.