Here's the complete, fixed implementation of the `JobQueue` class with proper locking to eliminate the race condition:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            if job_id not in self.jobs:
                return False

            data = self.jobs[job_id]
            result = processor(data)
            self.results[job_id] = result
            del self.jobs[job_id]
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes and Rationale

1. **Thread Safety via Locking:**
   - A single `threading.Lock()` is used to serialize access to shared state (`self.jobs` and `self.results`) across all methods.
   - This prevents race conditions such as concurrent writes to `self.results`, TOCTOU issues, and inconsistent reads/writes to `self.jobs`.

2. **Atomic Operations:**
   - The `process_job` method checks for job existence, retrieves data, processes it, stores the result, and deletes the job—all within a single lock acquisition. This ensures these steps are atomic and consistent.

3. **Tradeoff Consideration:**
   - Holding the lock during job processing simplifies implementation and ensures correctness, but it may reduce throughput for long-running tasks. For production systems with CPU-intensive jobs, consider releasing the lock during processing and using alternative synchronization strategies.

This implementation ensures that all accesses to shared state are protected, eliminating the concurrency bug.