Bibliographic record
Abstract
End user involvement and input into the design and evaluation of information systems has been recognized as being a critical success factor in the adoption of information systems. Nowhere is this need more critical than in the design of health information systems. Consistent with evidence from the general software engineering literature, the degree of user input into design of complex systems has been identified as one of the most important factors in the success or failure of complex information systems. The participatory approach goes beyond user-centered design and co-operative design approaches to include end users as more active participants in design ideas and decision making. Proponents of participatory approaches argue for greater end user participation in both design and evaluative processes. Evidence regarding the effectiveness of increased user involvement in design is explored in this contribution in the context of health IT. The contribution will discuss several approaches to including users in design and evaluation. Challenges in IT evaluation during participatory design will be described and explored along with several case studies.
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 | 0.308 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".