Lest Formalisms Impede Insight and Success: Evaluation in Health Informatics
Bibliographic record
Abstract
OBJECTIVES: To illustrate the advantages of an open-ended formative evaluation approach using a project-specific selection of methods over the controlled trial approach in the evaluation of health information systems. To illustrate factors leading to success and others impeding it in a telehealth project. METHODS: The methods and results of an evaluation of the BC Telehealth Program are summarized. RESULTS: The evaluation gave a comprehensive picture of the project, including assessment of the effects of an array of telehealth applications, and their economic impact. Factors leading to success and others preventing it are identified from the level of overall program management to the project specifics. The results include unanticipated effects and explanations for their reasons of occurrence. Neither the comprehensiveness of information nor the timeliness was achieved in a related project using a controlled trial approach. CONCLUSIONS: Not all types of health information system projects can be evaluated using the controlled trial approach. This approach may impede important insights. It is also usually much less efficient. Funding agencies and journal editors have to take this into account when selecting projects for funding and submissions for publication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.535 | 0.672 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".