Knowledge translation dataset: An e-health intervention for pregnancy in inflammatory bowel disease
Bibliographic record
Abstract
This article presents data collected from a cohort of patients with inflammatory bowel disease, who expressed interest in family planning and reproductive health in their clinical context. They were randomized (1:1, text-only vs. multimedia content) to access an online e-health portal containing educational information on the topic. The data collected includes baseline demographics, medication history, reproductive history, as well as standardized, validated questionnaires on knowledge ('CCPKnow'), reproductive concerns, beliefs about medications ('BMQ'), and medication adherence ('MARS-5'). These questionnaires were administered prior to the intervention, immediately after accessing the materials, and a minimum of 6 months later (without re-accessing the online material). Two publications have been generated from analysis and aggregation of the CCPKnow data ("Pregnancy-related Beliefs and Concerns of Inflammatory Bowel Disease Patients are Modified After Accessing e-Health Portal" (Sutton et al., in press), "Innovative Online Educational Portal Improves Disease-Specific Reproductive Knowledge Among Patients With Inflammatory Bowel Disease" (Sutton et al., 2018) however this is an extensive dataset that could be analyzed or combined with others' datasets for further insights.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".