The reality of applying an assessment guideline to a telemedicine mental health programme
Bibliographic record
Abstract
A guideline for assessment of telemedicine applications was used by the Alberta Mental Health Board (AMHB) in its evaluation of a telemedicine mental health (TMH) service. Many attributes referred to in the guideline were well covered in the AMHB evaluation. However, there were limitations on the assessment of outcomes and cost-effectiveness. From the perspective of the AMHB, the guideline was helpful, although more so in the earlier stages of the TMH service than for its appraisal as it reached maturity. The measures of performance suggested by the guideline did not fully match local operational conditions. Constraints on the assessment of the mature TMH service included the complexity of the network, the limited resources available for evaluation and the routine administrative demands of decision makers. This experience points to the usefulness of standardized assessment approaches to telemedicine, but also to their limitations.
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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 | 0.185 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".