Hip and Knee Replacement Patients' Experiences With an Orthopaedic Patient Navigator
Bibliographic record
Abstract
Hip and knee replacement surgery is common, yet more than 10% of patients who undergo total hip replacement (THR) and total knee replacement (TKR) report postsurgery dissatisfaction. Recommendations for improving patient experience after total joint replacement surgery include increasing support to patients, including having a patient navigator available to patients before and after surgery. This article reports on THR and TKR patients' experiences of using an orthopaedic patient navigator. We employed qualitative description to understand THR and TKR patients' experiences of interacting with an orthopaedic patient navigator in a community teaching hospital. Telephone interviews were conducted with 15 purposefully selected total joint replacement patients (TKR: n = 11; THR: n = 4) who had at least one contact with the navigator. Interview transcripts were analyzed using thematic analysis. Patients described receiving physical support services, emotional support services, informational support services, and care coordination services from the patient navigator. All interactions with the patient navigator were positive. Knowing the patient navigator was available for any future concerns also provided indirect benefits of reassurance, comfort, and security. Patients described these direct and indirect benefits as potentially having long-lasting and resilient positive effects. An orthopaedic patient navigator can have a positive impact on patients' THR and TKR experience and fill gaps in support identified in earlier studies. Addressing patients' complex and varied care needs is well suited to a clinical nurse specialist in the role. Investing in an orthopaedic patient navigator provides reassurance to patients that their needs are a priority and will be addressed in a timely manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".