You’ll know when you’re ready: a qualitative study exploring how patients decide when the time is right for joint replacement surgery
Bibliographic record
Abstract
BACKGROUND: While some studies have identified patient readiness as a key component in their decision whether to have total joint replacement surgery (TJR), none have examined how patients determine their readiness for surgery. The study purpose was to explore the concept of patient readiness and describe the factors patients consider when assessing their readiness for TJR. METHODS: Nine focus groups (4 pre-surgery, 5 post-surgery) were held in four Canadian cities. Participants had been either referred to or seen by an orthopaedic surgeon for TJR or had undergone TJR. The method of analysis was qualitative thematic analysis. RESULTS: There were 65 participants, 66% female and 34% male, 80% urban, with an average age of 65 years (SD 10). Readiness reflected both the surgeon's advice that the patient was clinically ready for surgery and the patient's feeling that they were both mentally and physically ready for surgery. Mental readiness was described as an internal state or feeling of being ready or prepared while physical readiness was described as being physically fit and in good shape for surgery. Factors associated with readiness included: 1) pain: its severity, the ability to cope with it, and how it affected their quality of life; 2) mental preparation; 3) physical preparation; 4) the optimal timing of surgery, including age, anticipated rate of deterioration, prosthesis lifespan and the length of the waiting list. CONCLUSIONS: Patient readiness should be assessed prior to TJR. By assessing patient readiness, health professionals can elucidate and deal with concerns and fears, understand and calibrate expectations, assess coping strategies, and use this information to help determine optimal timing, both before and after the surgical consultation.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".