Please assess the completion status of the following clinical trial:

Trial Title: Lung Cancer Screening Navigation for Homeless People

Study Design Characteristics:
- Allocation: RANDOMIZED
- Intervention Model: PARALLEL
- Observational Model: None
- Primary Purpose: SCREENING
- Time Perspective: None
- Masking: NONE
- Phase: None
- Number of Arms: 2.0

Eligibility Criteria:
Inclusion Criteria:~* Currently or formerly homeless~* Have a 30 pack-year smoking history and have smoked within the past 15 years~* Have a Boston Health Care for the Homeless (BHCHP) primary care provider (PCP)~* Proficient in English, assessed with items asking about native language and self-reported comfort communicating in English among non-native speakers~Exclusion Criteria:~* Prior chest computed tomography (CT) imaging in the past 12 months~* Personal history of lung cancer, or current presentation with symptoms concerning for lung cancer (e.g. hemoptysis or unexplained weight loss of \>15 lbs. in the past year)~* PCP is the principal investigator~* Inability to provide informed consent, assessed with knowledge questions about the material presented during the informed consent process that individuals must correctly answer before providing informed consent to participate

- Gender: ALL
- Minimum Age: 55 Years
- Maximum Age: 77 Years
- Healthy Volunteers: f

Study Arms:
- Usual care without patient navigation (Type: NO_INTERVENTION): Participants assigned to this arm will be given basic educational materials on general lung health and referred back to their primary care provider (PCP) for management as per usual practice.
- Usual care with patient navigation (Type: EXPERIMENTAL): Participants assigned to this arm will be informed about lung cancer screening (LCS), provided educational materials on LCS and patient navigation, and offered access to an LCS navigator who will partner with participants and primary care providers (PCPs) to facilitate low-dose computed tomography (LDCT) completion and follow-up.

Based on this trial design, predict the completion outcome and termination type (if applicable).