The eFOSTr PROJECT: Design, Implementation and Evaluation of a Web-based Personal Health Record to Support Health Professionals and Families of Children Undergoing Transplants
Bibliographic record
Abstract
We describe the eFOSTr PROJECT, which has involved the design, implementation and testing of a unique Internet-based Personal Health Record (PHR) to support the families of transplant children and their healthcare providers. There are many gaps in the way that information is stored for children undergoing or about to undergo transplants. This group of children presents the most challenging exercise in information support between geographic and institutionally separated medical teams. They are, however, supported by highly motivated parents and families in life-threatening circumstances. A PHR was designed that allows for secure data entry, data storage, and easy controlled data access by the children's guardians or parents. The record includes contact and team member names, and medical data such as growth charts, immunizations, allergies, medications, lab values and scanned or digitized medical reports. Families can record the progress of their child as they would with a paper binder and customize their child's record with a photograph gallery and Internet link section for personal and general interest. Extensive computer-based testing of the PHR is complete. The system is being evaluated to determine the extent to which it meets the information needs of families and health providers in differing situations across Canada. The effectiveness of the system as a means for providing continuity of information and education is also being assessed. To conduct these evaluations, new users are being interviewed and tracked in a qualitative longitudinal study. Characteristics of the needs of the transplant families known to the David Foster Foundation (DFF) in Canada are described so that comparisons can be made to other patient groups who could benefit from their own adapted and specialized PHRs.
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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 | 0.046 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".