Assessing patient satisfaction with Video-Observed Therapy (VOT) for drug-susceptible tuberculosis during the COVID-19 pandemic.
Bibliographic record
Abstract
Background: Video Observed Therapy (VOT) has been proposed as an alternative to Directly Observed Therapy (DOT) for tuberculosis (TB) treatment as prior studies have demonstrated similar adherence rates. This study evaluated patient experiences with VOT after it became a standard practice in Calgary, Alberta during the COVID-19 pandemic. Methods: Patients ≥ 18 years with drug-susceptible TB between March and December 2020 were asked to complete a patient satisfaction survey. Twenty-two survey statements were based on previously identified themes of service quality as it pertains to VOT (examples: confidentiality, convenience) with a Likert scale of agreement (1=strongly disagree, 5=strongly agree). Results: A total of 34 participants completed the survey out of 122 eligible patients (mean age 47 years, 50% females). The majority of participants strongly agreed that VOT was easy to use and convenient (median 5, IQR 1 for both). Participants disagreed that they had connection issues during VOT (median 2, IQR 2). 38% of participants preferred a VOT schedule outside of usual clinic hours (median 3, IQR 1). Participants strongly agreed that VOT maintained their confidentiality (median 5, IQR 1). 83% of participants would recommend VOT to other patients instead of DOT (median 5, IQR 1). Adherence compared to those on DOT during the pre-pandemic period did not differ (VOT 95.5% versus DOT 94.8%, p=0.74). Conclusion: Patient satisfaction rates were high with VOT. A significant proportion indicated a preference to take medications by VOT outside of usual clinic hours, highlighting asynchronous VOT as a potential intervention to further enhance patient-centered 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".