Evaluation Practices of a Major Canadian Telehealth Provider: Lessons and Future Directions for the Field
Bibliographic record
Abstract
The objective of this study was to assess the quality of Capital Health (Edmonton Area)'s telehealth evaluation practices. We conducted a comprehensive background literature review examining the current state of the art in telehealth evaluation. Using the Clinical, Human and Organizational, Educational, Administrative, Technical, and Social (CHEATS) evaluation framework, we examined 77 documents pertaining to 17 different pilot and continuing telehealth projects in Capital Health's Regional Telehealth Program. Capital Health's practices meet and reflect current standards in the field of telehealth evaluation. Strongest areas are Clinical, Technical, and Administrative evaluation, while Social and Human and Organizational evaluation are in need of the most development. Variation in quality and quantity of evaluation measures also makes direct comparisons between projects difficult. The CHEATS framework is both theoretically and practically appropriate for planning and conducting telehealth evaluations. Capital Health plans to adopt CHEATS to improve the quality of its future telehealth evaluation evidence.
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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 | 0.106 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".