Do “Virtual” and “Outpatient” Public Health Tuberculosis Clinics Perform Equally Well? A Program-Wide Evaluation in Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Meeting the challenge of tuberculosis (TB) elimination will require adopting new models of delivering patient-centered care customized to diverse settings and contexts. In areas of low incidence with cases spread out across jurisdictions and large geographic areas, a "virtual" model is attractive. However, whether "virtual" clinics and telemedicine deliver the same outcomes as face-to-face encounters in general and within the sphere of public health in particular, is unknown. This evidence is generated here by analyzing outcomes between the "virtual" and "outpatient" public health TB clinics in Alberta, a province of Western Canada with a large geographic area and relatively small population. METHODS: In response to the challenge of delivering equitable TB services over long distances and to hard to reach communities, Alberta established three public health clinics for the delivery of its program: two outpatient serving major metropolitan areas, and one virtual serving mainly rural areas. The virtual clinic receives paper-based or electronic referrals and generates directives which are acted upon by local providers. Clinics are staffed by dedicated public health nurses and university-based TB physicians. Performance of the two types of clinics is compared between the years 2008 and 2012 using 16 case management and treatment outcome indicators and 12 contact management indicators. FINDINGS: In the outpatient and virtual clinics, respectively, 691 and 150 cases and their contacts were managed. Individually and together both types of clinics met most performance targets. Compared to outpatient clinics, virtual clinic performance was comparable, superior and inferior in 22, 3, and 3 indicators, respectively. CONCLUSIONS: Outpatient and virtual public health TB clinics perform equally well. In low incidence settings a combination of the two clinic types has the potential to address issues around equitable service delivery and declining expertise.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".