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Machine Learning-Based Identification of Impostor Phenomenon Subtypes and Management Strategies for Intensive Care Unit Nurses

Machine Learning-Based Identification of Impostor Phenomenon Subtypes and Management Strategies for Intensive Care Unit Nurses

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500115238
Enrollment
Unknown
Registered
2025-12-24
Start date
2026-01-01
Completion date
Unknown
Last updated
2026-04-14

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

None

Interventions

Observation group:None

Sponsors

The First Affiliated Hospital of Shantou University Medical College
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1.Formally employed or contractual ICU nurses: Engaged in frontline clinical work and participating in ward shift rotations; 2. Having worked in the intensive care unit for more than 3 months; 3.Those who voluntarily participate in the study and sign the informed consent form.

Exclusion criteria

Exclusion criteria: 1. Pregnancy or diagnosis with a psychiatric disorder; 2. Refusal to participate in the study.

Design outcomes

Primary

MeasureTime frame
Impostor phenomenon;Psychological resilience;—

Secondary

MeasureTime frame
Work resources and work demands;The General Self-Efficacy;—

Countries

China

Contacts

Public ContactSugen He

The First Affiliated Hospital of Shantou University Medical College

27476648@qq.com+86 135 9286 8793

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 17, 2026