Eliciting patient views on choosing the next available surgeon to reduce waiting times for joint replacement surgery: on the need to consider individual patient preferences and information needs.
Bibliographic record
Abstract
Rationale, aims, and objectives: The objective is to understand patient views on surgeon choice when being referred for joint replacement; do they prefer referral to the next available surgeon for a shorter waiting time or do they prefer their own choice of surgeon? What factors guide this decision and what information would patients find useful when deciding?Methods: Seven focus groups were held in four Canadian cities. Participants had been either referred to or seen by an orthopaedic surgeon for hip or knee replacement surgery. The method of analysis was qualitative thematic analysis.Results: There were 50 participants, 66% female, 60% knee replacements and the average age was 66 years (SD 12). 68% were on a waiting list for surgery and 32% were undecided about surgery. Although patients varied in their views, many wanted some choice in their decision about a surgeon. If patients were in severe pain, they were more likely to accept the idea of referral to the next available surgeon. Other considerations were the recommendation of their family physician, surgeon reputation and confidence in the surgeon. Although some patients wanted access to patient outcome data and satisfaction data, others were skeptical of data and the interpretation of statistics. Conclusion: Patient views on referral to the next available surgeon are relevant to initiatives aimed at improving access to care, such as pooled surgeon waiting lists. If implemented, waiting time is not the only important factor for patients. Individual patient preferences and information needs must also be considered, given their pivotal significance in the development of person-centered approaches in clinical care.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".