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Careviv compares confirmed doctor and clinic information across the factors that matter—then explains what aligns, what needs discussion, and what remains unknown.
Hard constraints that can stop a pairing before anyone discusses fit: licensing route, specialty, province, work-authorisation, supervision capacity, and timing that makes a start impossible. Outcomes are pass, fail, or unknown, each with a reason.
How closely the doctor’s and clinic’s stated preferences align across practice model, workload, schedule, compensation structure, clinical interests, location, languages, and working preferences. Each relevant factor is labelled aligned, possible conflict, unknown, needs confirmation, or not applicable.
How close each side is to a viable start. Doctor readiness covers licensing and relocation pathway progress. Clinic readiness covers hiring horizon, supervision capacity, and whether opportunity facts are complete. These stay separate from mutual alignment.
How complete and current the inputs are. Missing, stale, or unverified facts lower data confidence. They do not silently count as a match.
Confirmed structured doctor and clinic information, including specialty, experience, practice model, location, schedule, workload, compensation structure, timing, and stated priorities.
Each relevant factor is labelled aligned, possible conflict, unknown, needs confirmation, or not applicable.
Eligibility, readiness, and data confidence remain separate from mutual alignment. Protected characteristics and private household information are excluded from matching.
Results help doctors and clinics ask better questions and decide whether to continue. Careviv does not make hiring, career, licensing, or immigration decisions.
Recruitment profiles and matching free text are not currently sent to an LLM. Limited AI features used elsewhere do not rank or select recruitment matches.
Matching FAQ
Careviv presents an explainable fit score alongside factor-level alignment. Each factor explains what appears aligned, what may conflict, what remains unknown, and what needs confirmation. Missing answers stay unknown and do not raise the score.
A versioned model compares confirmed structured doctor and clinic information using consistent matching rules. The same confirmed inputs produce the same factor-level result under the same model version.
Matching can consider job-relevant factors such as specialty, experience, practice model, schedule, workload, compensation structure, location, clinical interests, team support, timing, and the priorities each side identifies as essential, preferred, or flexible.
No. Eligibility checks essential requirements such as specialty, licensing pathway, work authorization, and supervision needs. Readiness considers availability, hiring timing, licensing progress, and relocation steps. These remain separate so practical constraints are not confused with mutual alignment.
Missing, outdated, or conflicting information is shown as unknown and lowers data confidence. It is never silently treated as alignment. Careviv identifies what still needs confirmation before either side decides whether to proceed.
No. Careviv compares confirmed information and explains alignment. Doctors choose which opportunities to explore, and clinics choose whom to interview or hire. Every career and hiring decision remains with the people involved.
No. Recruitment profiles and matching free text are not currently sent to an LLM. If AI-assisted interpretation is introduced later, Careviv will update its disclosures and implement the required confirmation and privacy safeguards first.
Protected characteristics and private household information do not influence matching. Information voluntarily shared for relocation support remains separate from hiring recommendations. Careviv does not use the model to infer personality, physician quality, or sensitive personal characteristics.
Yes. Users can update the information used by the model when something is missing, outdated, or incorrect. Later matching assessments then use the updated information. Users can also raise a result that appears inaccurate or incomplete for review.
Profiles are private by default. Doctors and clinics control their visibility and choose which introductions move forward. Identifiable information is shared according to the relevant visibility, consent, and introduction settings.
