Assessing the Educational Needs of Canadians with Systemic Sclerosis
Bibliographic record
Abstract
To the Editor: Changes in appearance, significant morbidity, and the absence of disease-modifying therapies may lead to psychosocial issues in patients with systemic sclerosis (SSc)1,2,3,4. Limited education may contribute to poor medication adherence5. Education of patients could potentially mediate some of these outcomes2,6 because patient education in rheumatic disease improves self-efficacy and self-management7,8. An Educational Needs Assessment Tool (ENAT) was developed to assess the perceived educational needs of people with rheumatic disease9, and has been validated in patients with SSc10. We used the ENAT to survey a sample of Canadians with SSc to understand their educational need(s) to inform educational initiatives and future research. This project was not deemed to require ethics approval by the Hamilton Integrated Research Ethics Board, and thus written consent was not required. The ENAT questionnaire was posted on the Scleroderma Society of Ontario and Scleroderma Society of Canada social media accounts in August 2017. Patients from 2 clinics were also provided with the online survey link. … Address correspondence to Dr. T. Semalulu, Department of Medicine, Internal Medicine Training Program, McMaster University Medical Centre, Room 1K11, 1200 Main St. West, Hamilton, Ontario L8N 3Z5, Canada. E-mail: teresa.semalulu{at}medportal.ca
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".