Open Information Management in User-driven Health Care
Bibliographic record
Abstract
This chapter discusses the role of open health information management in the the development of a novel, adaptable mixed-platform for supporting health care informational needs. This platform enables clients (patient users) requiring healthcare to enter an unstructured but detailed account of their dayto- day health information requirements that may be structured into a lifetime electronic health record. It illustrates the discussion with an operational model and a pilot project in order to begin to explore the potential of a collaborative network of patient and health professional users to support the provision of health care services, and helping to effectively engage patient users with their own healthcare. Such a solution has the potential to allow both patient and health professional users to produce useful materials, to contribute to improved social health outcomes in terms of health education and primary disease prevention, and to address both pre-treatment and post-treatment phases of illness that are often neglected in the context of overburdened support services.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".