An evaluation of Alberta retina health service delivery in an office setting: a cross-sectional survey of patient experience
Bibliographic record
Abstract
BACKGROUND: Retina sub-specialists provide much of the retina related eye care across Canada. In the province of Alberta, 18 retina sub-specialists work across six different offices. The purpose of this study was to assess the quality of care provided by Alberta retina sub-specialists in an office setting by administering a patient satisfaction survey. The results of this survey were provided to the same retina specialists to promote improvements in patient-centered health care delivery. METHODS: A cross sectional patient satisfaction survey was performed using a thirty-part questionnaire developed in collaboration with the Physician Learning Program at the University of Alberta. The survey was modelled after other similar patient satisfaction surveys used in other areas of medicine. Patients from ten of the eighteen retina practices in Alberta participated in this survey. Topics of the survey included pre-appointment experience, physician-patient interactions and quality, comments/ feedback and patient demographics. RESULTS: 214 randomly sampled patients completed the survey from three geographically separate office locations in Calgary and Edmonton. 90% of patients responded that their retina sub-specialist listened adequately and provided quality care in a timely manner. Patients felt that there could be improvements to accessibility to the clinic and reduced wait times, as well as in the pre-operative consent process. Including a more complete explanation of the procedure as well as the potential risks and benefits. Only 51% of patients felt that the risks of a potential surgery had been adequately explained to them. There was a statistically significant association found between overall satisfaction and lower wait times, understanding of procedural risks and time with, listening to and involving the patient in care. There were no correlations found with other demographics such as ethnicity, sex, distance traveled or age. CONCLUSIONS: This patient satisfaction survey provided valuable patient care feedback to the retina sub-specialists of Alberta. The survey results will assist this group to improve the consent process and thereby improve patient centered health care delivery. We would recommend the distribution of this survey or other similar patient satisfaction questionnaire by retina sub-specialists to their patients to improve patient centered care in their clinics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".