Opioid use disorder patients’ perceptions of healthcare delivery platforms
Bibliographic record
Abstract
OBJECTIVES: To assess the acceptability and quality of web-based videoconferencing telemedicine consultation platform in the treatment of opioid use disorder at TrueNorth Medical Centre. METHODS: We conducted an interview based quality improvement initiative using an investigator-designed questionnaire. The questionnaire consisted of 17 Agree/Disagree questions, measured on a 7-point Likert scale and 2 questions where patients had the ability to elaborate qualitatively on their perceptions and experiences with their telemedicine service. Content-style analysis was performed on qualitative responses. RESULTS: The majority of patients (n=14; 47%) preferred face-to-face over telemedicine consultations. The number of patients that preferred telemedicine consultations over face-to-face consultations was lower (n=6; 20%). A notable number of patients (n=10; 33%) indicated no specific preference for either telemedicine or face-to-face consultations. Patients preferring face-to-face consultations rated their clinical outcome and patient-physician relationship following telemedicine consultations similarly as those who preferred telemedicine consultations. Patients preferring telemedicine rated their experience and overall perceptions of the service significantly higher than those preferring face-to-face consultations. Patients who preferred telemedicine consultations identified the efficient and timesaving nature of telemedicine consultations as primary advantages whereas those preferring face-to-face consultations reported lower levels of empathy from their physician during telemedicine consultations as a major disadvantage. CONCLUSIONS: The majority of patients at TrueNorth Medical Centre viewed telemedicine consultations as an acceptable treatment modality. Patients preferring telemedicine consultations and those preferring face-to-face consultations evaluated the majority of the measured indices of care in a similar fashion.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".