Delivery of menopause care during a pandemic: an evaluation of patient satisfaction with telephone visits
Bibliographic record
Abstract
Abstract Objective: We aimed to evaluate patient satisfaction with telephone appointments during the first wave of the COVID-19 pandemic, determine visit type preference (in-person vs telephone), and predictors of those preferences. Methods: In this cross-sectional study, patient visits during the first wave of COVID-19 (March 20 to July 15, 2020) were characterized (in-person vs telephone) in a single provider's weekly menopause clinic in Toronto, Canada. Patients attending telephone appointments were asked to complete a modified Telemedicine Satisfaction Questionnaire with 5-point Likert-scale responses. Demographic information was collected along with the patient-reported cost to attend an in-person appointment (monetary, travel time, and time away from work). Of those who experienced both visit types, preference was evaluated and bivariate analysis was performed identifying factors associated with visit type preference and included in a multivariable binary logistic regression model. Results: During the first wave of the COVID-19 pandemic, 214 women had 246 visits, attending mostly by telephone (221/246, 90%). Mean Telemedicine Satisfaction Questionnaire composite score was 4.23 ± 0.72. Of those who attended a prepandemic in-person appointment (118/139, 85%), a minority (24/118, 20%) preferred in-person visits. Those favoring in-person were more likely to commute less than 30 minutes (OR 3.78, 95% CI 1.16-12.29, P = 0.027), require less than 2 hours away from work (OR 4.05, 95% CI 1.07-15.4, P = 0.04), and spend less than $10 to attend (OR 3.67, 95% CI 1.1-12.26, P = 0.035). Conclusions: Menopause clinic telephone appointments had high patient satisfaction, with most preferring this visit type, although in-person visits are preferred among a minority of women.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".