Future Directions in Evaluation Research: People, Organizational, and Social Issues
Bibliographic record
Abstract
OBJECTIVE: To review evaluation literature concerning people, organizational, and social issues and provide recommendations for future research. METHOD: Analyze this research and make recommendations. RESULTS AND CONCLUSIONS: Evaluation research is key in identifying how people, organizational, and social issues - all crucial to system design, development, implementation, and use - interplay with informatics projects. Building on a long history of contributions and using a variety of methods, researchers continue developing evaluation theories and methods while producing significant interesting studies. We recommend that future research: 1) Address concerns of the many individuals involved in or affected by informatics applications. 2) Conduct studies in different type and size sites, and with different scopes of systems and different groups of users. Do multi-site or multi-system comparative studies. 3) Incorporate evaluation into all phases of a project. 4) Study failures, partial successes, and changes in project definition or outcome. 5) Employ evaluation approaches that take account of the shifting nature of health care and project environments, and do formative evaluations. 6) Incorporate people, social, organizational, cultural, and concomitant ethical issues into the mainstream of medical informatics. 7) Diversify research approaches and continue to develop new approaches. 8) Conduct investigations at different levels of analysis. 9) Integrate findings from different applications and contextual settings, different areas of health care, studies in other disciplines, and also work that is not published in traditional research outlets. 10) Develop and test theory to inform both further evaluation research and informatics practice.
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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 | 0.425 | 0.492 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.027 | 0.061 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".