M15 Patient satisfaction in a tertiary cough service
Bibliographic record
Abstract
Introduction Patient satisfaction surveys (PSS) can help identify ways of improving practice and facilitate better quality care. Patient opinion in health services research is integral but data from chronic cough populations is unknown. Aim To identify patient satisfaction in our tertiary cough service. Methods We devised a PSS containing 19 structured questions. Patients attending review consultations in two consecutive clinics were asked to consider completing the anonymous PSS. Results Fifty-two PSS were completed; an 84% response rate. Of those 43 had full responses for analysis [79% female, 58%≥55 years in age]. Patient satisfaction was extremely high (figure 1); 70% thought the care received was excellent and 95% were likely to recommend the service to friends and family. Improvement suggestions related to parking and appointment management. However 44% felt clinic locality was inconvenient, but the majority (63%) of those were not interested in Skype review consultations; response was unrelated to age. Conclusion To our knowledge, this is the first reported patient satisfaction data in chronic cough patients. Despite the refractory nature of the condition, patient satisfaction is extremely high. As a quarter of our service’s patients travel ≥25 miles, the inconvenience of clinic accessibility is not surprising. Nonetheless, patients appear to value face to face consultations and further patient consultation is required before utilising tele-health.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".